Neural rendering for inverse graphics generation

ABSTRACT

Approaches are presented for training an inverse graphics network. An image synthesis network can generate training data for an inverse graphics network. In turn, the inverse graphics network can teach the synthesis network about the physical three-dimensional (3D) controls. Such an approach can provide for accurate 3D reconstruction of objects from 2D images using the trained inverse graphics network, while requiring little annotation of the provided training data. Such an approach can extract and disentangle 3D knowledge learned by generative models by utilizing differentiable renderers, enabling a disentangled generative model to function as a controllable 3D “neural renderer,” complementing traditional graphics renderers.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationSer. No. 62/986,618, filed Mar. 6, 2020, and entitled “3D NeuralRendering and Inverse Graphics with StyleGAN Renderer,” which is herebyincorporated herein in its entirety and for all purposes.

This application is also related to co-pending U.S. patent applicationSer. No. 17/019,120, filed Sep. 11, 2020, and entitled “Labeling ImagesUsing a Neural Network,” as well as co-pending U.S. patent applicationSer. No. 17/020,649, filed Sep. 14, 2020, and entitled “GeneratingLabels for Synthetic Images Using One or More Neural Networks,” each ofwhich is hereby incorporated herein in its entirety and for allpurposes.

BACKGROUND

A variety of different industries rely upon three-dimensional (3D)modeling for various purposes, including those that require thegeneration of representations of 3D environments. In order to providerealistic complex environments, it is necessary to have a variety ofdifferent types of objects, or similar objects with differentappearances, to avoid unrealistic repetition or omissions.Unfortunately, obtaining a large number of three dimensional models canbe a complex, expensive, and time (and resource) intensive process. Itmay be desirable to generate 3D environments from a large amount ofavailable two-dimensional (2D) data, but many existing approaches do notprovide for adequate 3D model generation based on 2D data. Forapproaches that prove promising, such as may involve machine learning,it is still necessary to have a sufficiently large number and variety oflabeled training data in order to train the machine learning. Aninsufficient number and variety of annotated training data instances canprevent a model from being sufficiently trained to produce acceptablyaccurate results.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments in accordance with the present disclosure will bedescribed with reference to the drawings, in which:

FIGS. 1A, 1B, and 1C illustrate image data that can be utilized,according to at least one embodiment;

FIG. 2 illustrates a neural rendering and inverse graphics pipeline,according to at least one embodiment;

FIG. 3 illustrates components of a an image generation system, accordingto at least one embodiment;

FIG. 4 illustrates representative points used to determine view or poseinformation for an object, according to at least one embodiment;

FIG. 5 illustrates a process for training an inverse graphics network,according to at least one embodiment;

FIG. 6 illustrates components of a system for training and utilizing aninverse graphics network, according to at least one embodiment;

FIG. 7A illustrates inference and/or training logic, according to atleast one embodiment;

FIG. 7B illustrates inference and/or training logic, according to atleast one embodiment;

FIG. 8 illustrates an example data center system, according to at leastone embodiment;

FIG. 9 illustrates a computer system, according to at least oneembodiment;

FIG. 10 illustrates a computer system, according to at least oneembodiment;

FIG. 11 illustrates at least portions of a graphics processor, accordingto one or more embodiments;

FIG. 12 illustrates at least portions of a graphics processor, accordingto one or more embodiments;

FIG. 13 is an example data flow diagram for an advanced computingpipeline, in accordance with at least one embodiment;

FIG. 14 is a system diagram for an example system for training,adapting, instantiating and deploying machine learning models in anadvanced computing pipeline, in accordance with at least one embodiment;and

FIGS. 15A and 15B illustrate a data flow diagram for a process to traina machine learning model, as well as client-server architecture toenhance annotation tools with pre-trained annotation models, inaccordance with at least one embodiment.

DETAILED DESCRIPTION

Approaches in accordance with various embodiments can provide for thegeneration of three-dimensional (3D) models, or at least the inferencingof one or more 3D properties (e.g., shape, texture, or light), fromtwo-dimensional (2D) input, such as images or video frames. Inparticular, various embodiments provide approaches to train an inversegraphics model to generate accurate 3D models or representations from 2Dimages. In at least some embodiments, a generative model can be used togenerate multiple views of an object from different viewpoints or withdifferent poses, with other image features or aspects being kept fixed.The generated images can include pose, view, or camera information thatwas used to generate each such image. These images can be used astraining images for an inverse graphics network. The inverse graphicsnetwork can utilize such a set of input images for a single object togenerate and refine a model for that object. In at least someembodiments, these networks can be trained together, where a 3D modeloutput by the inverse graphics network can be fed as input training datato the generator network for purposes of improving the accuracy of thegenerator network. A combined loss function can be used with terms forboth the inverse graphics network and the generator network, in order tooptimize both networks together using a common set of training data.Such an approach can provide for improved 3D reconstruction performancewith respect to prior approaches, can provide for categorygeneralization (i.e., a model does not need to be trained for only aspecific type of class of object), and can significantly reduce a needfor manual annotation (e.g., from many hours to a minute or less).

In at least one embodiment, an inverse graphics network can be trainedto generate a 3D model or representation, or otherwise determine 3Dinformation, from a 2D image of an object, such as the input image 100of FIG. 1A that illustrates a front view of a vehicle. As can beappreciated, the input image provides at least three challenges forreconstructing a 3D model or representation of that vehicle. A firstchallenge is that there is only image data for the front of the vehiclein this example, so image data for other portions of the vehicle (e.g.,the back, underneath, or sides) would need to be completely inferred andgenerated. For example, other view images 140 are illustrated in FIG. 1Bwhich show different views of the sides of the vehicle, where portionsof the side of the vehicle are represented that were not included in theoriginal input image. Another challenge is that there is no depthinformation in the 2D image. Any depth or shape information must beinferred based only on the two-dimensional single view. Yet anotherchallenge is that there is no camera or pose information or annotationprovided with the input image 100, to use as a reference when instructedto generate other views or portions of a 3D model.

In order to train a model, network, or algorithm (e.g., an inversegraphics model) to generate or infer such 3D information, includingsemantic information, approaches in accordance with various embodimentscan provide view images 140 such as those illustrated in FIG. 1B astraining images for a network. As mentioned, however, it can bedifficult to obtain a sufficient number of views for a given object, andeven if a sufficient number is obtained, those images must be annotatedwith sufficient information, such as pose, camera, or view data, toenable those images to function as ground truth training data.

Approaches in accordance with various embodiments can utilize agenerator that is able to take an input image, such as image 100 of FIG.1A, and generate a set of images of that object in different views. Inat least one embodiment, this can be a generative neural network, suchas a generative adversarial network (GAN). A GAN can be provided with aninput image as well as pose, view, or camera data, and can generate animage representation of an object in the input image having, orcorresponding to, the provided pose, view, or camera data. In order togenerate a set of training image for this object, a set of pose, view,or camera information can be provided (manually, randomly, or accordingto a specified pattern or rule) and an image can be generated for eachunique pose, view, or camera input. Another advantage of such a processis that the GAN can provide the annotations without any additionalprocessing, as the pose, view, or camera information was already knownand used in generating the images. An example GAN that can be used forsuch a purpose is a style generative adversarial network, also referredto as StyleGAN, developed by NVIDIA Corporation. According to one ormore embodiments, a StyleGAN model extends the general GAN architectureto include a mapping network to map points in latent space to anintermediate latent space, which can control the “style” (e.g., pose,view, or camera information) at each point in the generator model, andcan also introduce noise as a source of variation at each of thesepoints. In one or more embodiments, a StyleGAN model can be used togenerate large, accurate sets of multi-view data in short time and withrelatively little processing resources required. A StyleGAN model cantake an input image 140 and generate multiple different view images 140for use in training an inverse graphics network. The inverse graphicsnetwork can then be trained to take 2D input images and generate orinfer relatively accurate 3D information 180 as illustrated in FIG. 1C,such as may relate to the shape of an object (as may be represented by a3D mesh), the lighting of the object (e.g., direction, intensity, andcolor of one or more light sources), and the texture of the object(e.g., a complete set of image data representing all relevant portionsof the object. As known for applications such as computer graphics, aview of an object can be generated by projecting the texture onto themesh, lighting the mesh using the appropriate lighting information, thenrendering an image of that object from a determined point of view of avirtual camera. Other sets or types of 3D information may be used aswell for different use cases, applications, or embodiments. The 3Dinformation generated by the inverse graphics network can then, in turn,be used as training data for the StyleGAN model. These networks can betrained separately using separate loss functions or together using acommon loss function in various embodiments.

FIG. 2 illustrates an example training pipeline 200 that can be utilizedin accordance with various embodiments. This example pipeline 200includes two different renderers. The first renderer is a generatornetwork, such as a GAN (e.g., StyleGAN), and the second renderer in thisexample is a differentiable graphics renderer, such as aninterpolation-based differentiable renderer (DIB-R). A DIB-R is adifferentiable rendering framework that allows gradients to beanalytically computed for all pixels in an image. This framework canview foreground rasterization as a weighted interpolation of localproperties and background rasterization as a distance-based aggregationof global geometry, allowing for accurate optimization over vertexpositions, colors, normals, light directions, and texture coordinatesthrough a variety of lighting models. In this example, the generator 204is used as a synthetic data generator with efficient annotation of amulti-view dataset 206. This dataset can then be used to train aninverse graphics network 212 that predicts 3D properties from the 2Dimages. This network can be used to disentangle the latent code of thegenerator through a carefully designed mapping network.

In this example, input images of the multi-view dataset can be providedas input to the inverse graphics network 208, which can utilize aninferencing network to infer 3D information such as the shape, lighting,and texture of an object in the image. For training purposes, this 3Dinformation can be fed as input, along with the camera information fromthe input image, to a differentiable graphics renderer. This renderercan utilize the 3D information to generate shape information, a 3Dmodel, or one or more images for given camera information. Theserenderings can then be compared against the relevant ground truth data,using an appropriate loss function, to determine the loss values. Theloss can then be used to adjust one or more network weights orparameters. As mentioned, the output of the inverse graphics network 208can also be used to further train or fine tune the generator 204, orStyleGAN, to generate more accurate images.

FIG. 3 illustrates a portion 300 of such a pipeline that can be utilizedin accordance with at least one embodiment. As mentioned with respect toFIG. 2, 3D information (or “codes”) about an object in an image can beinferred, as may include a mesh 304, texture 306, and backgroundinformation 308. Camera information can also be utilized that wasextracted from one or more annotations of the input image. In thisexample the mesh 304 is processed using a machine learning processor(MLP) to extract one or more dimensions or latent features. The texture306 and background data 308 can be processed with respective encoders314, 316, such as one or more convolutional neural networks (CNNs), toalso extract the relevant dimensions or features. These features ordimensions can then be passed to a disentangling module 318 thatincludes a mapping network. The mapping network will attempt to map thefeatures of the various codes into a single latent code 320 that beprovided to the generator 322, or renderer, for generating an outputimage 322. In at least one embodiment, a portion of a latent code willcorrespond to the camera information, and the rest will correspond tothe mesh, texture, and background. An attempt can be made to merge thefeatures into a single set of features, instead of three sets offeatures for the mesh, texture, and background, before generating thelatent code 320. A selection matrix S can be used to select and mergethese features, along with the camera features, into the latent code320. This information can then be provided to the generator, such as aStyleGAN, for rendering an image. The latent code may also take otherforms, such as a latent space or feature vector of a determineddimension, such as for around 500 features. The entry for each dimensioncan contain information about the image, but it may be unknown whichfeature or dimension controls or contains which information or aspect ofthe image. In order to generate different image views of the sameobject, the features corresponding to the camera (which are known—saythe first 100 features) can be varied while leaving the other featuresfixed. Such an approach can ensure that only the view or pose changeswhile other aspects of the object or image remain unchanged betweenrendered images.

In at least some embodiments, there may need to be at least some type ofcamera or object view or pose information provided as annotations. FIG.4 illustrates one example approach to providing annotation points 402for a type of object in a set 400 of image views. Instead of labelingall key points for an object, which can take a lot of effort, weakcamera information can be utilized which only includes a subset ofpotential features. In this example, the camera pose can be split into anumber of different ranges, such as twelve ranges. Given these featurepoints and the pose ranges, this can be sufficient to obtain a roughestimate of the object position for use in annotating the image for useas training data. The process can then initialize from this weaklyaccurate camera pose information. Such a process can work with multipletypes of objects as well, such as people, animals, vehicles, and so on.

Approaches in accordance with various embodiments can thus utilizedifferentiable rendering to assist in the training of one or more neuralnetworks to perform inverse graphics-related tasks, as may include(without limitation) tasks such as predicting 3D geometry from monocular(e.g., 2D) photographs. To train high performing models, manyconventional approaches rely on multi-view imagery which is not readilyavailable in practice. Recent Generative Adversarial Networks (GANs)that synthesize images, in contrast, seem to acquire 3D knowledgeimplicitly during training: object viewpoints can be manipulated bysimply manipulating the latent codes. However, these latent codes oftenlack further physical interpretation and thus GANs cannot easily beinverted to perform explicit 3D reasoning. 3D knowledge learned bygenerative models can be extracted and disentangled, at least in part,by utilizing differentiable renderers. In at least one embodiment, oneor more generative adversarial networks (GANs) can be exploited as amulti-view data generator to train an inverse graphics network. This canbe performed using an off-the-shelf differentiable renderer, and thetrained inverse graphics network as a teacher to disentangle the latentcode of the GAN into interpretable 3D properties. In various approaches,an entire architecture can be trained iteratively using cycleconsistency losses. Such an approach can provide significantly improvedperformance with respect to conventional inverse graphics networkstrained on existing datasets, both quantitatively and via user studies.Such a disentangled GAN can also be utilized as a controllable 3D“neural renderer,” which can be used to complement traditional graphicsrenderers.

An ability to infer 3D properties such as geometry, texture, material,and light from photographs is key in many domains such as AR/VR,robotics, architecture, and computer vision. Interest in this problemhas been explosive, particularly in the past few years, as evidenced bya large body of published works and several released 3D libraries. Theprocess of going from images to 3D is often called “inverse graphics,”since the problem is inverse to the process of rendering in graphics inwhich a 3D scene is projected onto a 2D image by considering thegeometry and material properties of objects, and light sources presentin the scene. Most work on inverse graphics assumes that 3D labels areavailable during training, and trains a neural network to predict theselabels. To ensure high quality 3D ground-truth, synthetic datasets suchas ShapeNet are typically used. However, models trained on syntheticdatasets often struggle on real photographs due to the domain gap withsynthetic imagery.

To circumvent at least some of these issues, an alternative approach totraining an inverse graphics networks can be used that sidesteps theneed for 3D ground-truth during training. Graphics renderers can be madedifferentiable, which allows one to infer 3D properties directly fromimages using gradient based optimization. At least some of theseapproaches can employ a neural network to predict geometry, texture, andlight from images by minimizing the difference between the input imageand the image rendered from these properties. While impressive resultshave been obtained in some approaches, many of these approaches stillrequire some form of implicit 3D supervision, such as may utilizemulti-view images of the same object with known cameras. On the otherhand, generative models of images appear to learn 3D informationimplicitly, where manipulating the latent code can produce images of thesame scene from a different viewpoint. However, the learned latent spacetypically lacks physical interpretation and is usually not disentangled,where properties such as the 3D shape and color of the object oftencannot be manipulated independently.

Approaches in accordance with at least one embodiment can extract anddisentangle 3D knowledge learned by generative models by utilizingdifferentiable graphics renderers. In at least one embodiment, agenerator, such as a GAN, can be exploited as a generator of multi-viewimagery to train an inverse graphics neural network using adifferentiable renderer. In turn, the inverse graphics network can beused to inform the generator about the image formation process throughthe knowledge from graphics, effectively disentangling the latent spaceof the GAN. In at least one embodiment, a GAN (e.g., StyleGAN) can beconnected with an inverse graphics network to form a single architecturethat can be iteratively trained using cycle-consistency losses. Such anapproach can produce a trained network that can significantly outperforminverse graphics networks on existing datasets, and can provide forcontrollable 3D generation and manipulation of imagery using adisentangled generative model.

A pipeline that can be used for such an approach was describedpreviously with respect to FIG. 2. Such an approach can combine twotypes of renderers: a GAN-based neural “renderer” and a differentiablegraphics renderer. In at least one embodiment, such an approach canleverage the fact that a GAN can learn to produce highly-realisticimages of objects, and allows for a reliable control over the virtualcamera used to generate views of those objects. A set of camera viewscan be selected, manually or otherwise, such with rough viewpointannotation. A GAN, such as StyleGAN, can then be used to generate alarge number of examples per view. Such a dataset can be used to trainan inverse graphics network utilizing a differentiable renderer, such asDIB-R. A trained inverse graphics network can be used to disentangle thelatent code of the GAN, and turn the GAN into a 3D neural renderer,allowing for control over explicit 3D properties.

In at least one embodiment, a generator such as a StyleGAN model can beused to generate multi-view imagery. An example StyleGAN model is a 16layer neural network that maps a latent code z E Z drawn from a normaldistribution into a realistic image. The code z is first mapped to anintermediate latent code w E W which is transformed to w*=(w₁*, w₂*, . .. , w₁₆*)∈W* through 16 learned affine transformations. W* is referredto as the transformed latent space to differentiate it from theintermediate latent space W. Transformed latent codes w* are theninjected as the style information to a StyleGAN Synthesis network.

Different layers can control different image attributes in thegenerator. Styles in early layers adjust the camera viewpoint whilestyles in the intermediate and higher layers influence shape, texture,and background. It was empirically determined that the latent codew_(ν)*:=(w₁*, w₂*, w₃, w₄*) in the first four layers controls cameraviewpoints in at least one StyleGAN model. That is, if a process samplesa new code w_(ν)* but keeps the remaining dimensions of w* fixed (whichis referred to as the content code), images of the same object depictedin a different viewpoint can be generated. It can further be observedthat a sampled code w_(ν)* in fact represents a fixed camera viewpoint.That is, if w_(ν)* is kept fixed but the remaining dimensions of w*sampled, the generator can produce imagery of different objects in thesame camera viewpoint. The objects in each of the viewpoints will bealigned, as this generator is functioning as a multi-view datagenerator.

In an example approach, several views can be manually selected thatcover all the common viewpoints of an object ranging from 0-360 degreesin azimuth and roughly 0-30 degrees in elevation. Such an approach maypay attention to choose viewpoints in which the objects appear mostconsistent. Since inverse graphics techniques generally utilize camerapose information, the chosen viewpoint codes can be annotated with arough absolute camera pose. To be specific, each viewpoint code may beclassified into, for example, one of twelve azimuth angles, uniformlysampled along 360 degree increments. Each code can be assigned code afixed elevation (e.g., 0°) and camera distance. These camera poses canprovide a very coarse annotation of the actual pose, as the annotationserves as the initialization of the camera which will be optimizedduring training. Such an approach provides for annotation of all views,and thus the entire dataset, in a relatively short period of time, suchas one minute or less. Such a result can make the annotation efforteffectively negligible. For each viewpoint, a large number of contentcodes can be sampled to synthesize different objects in these views.Since a differentiable renderer, such as DIB-R, can also utilizesegmentation masks during training, a network such as MaskRCNN can befurther applied to obtain instance segmentation in the generateddataset. As a generator may sometimes generate unrealistic images orimages with multiple objects, images which have more than one instance,or small masks (less than 10% of the whole image area), can be filteredout in at least one embodiment.

Approaches in accordance with at least one embodiment can aim to train a3D prediction network f, parameterized by θ, to infer 3D shapes (as maybe represented as meshes) along with textures from images. Let I_(v)denote an image in viewpoint V from a generator dataset, and M itscorresponding object mask. The inverse graphics network makes aprediction as follows: {S, T} f_(θ)=(I_(V)) where S denotes thepredicted shape, and T a texture map. Shape S is deformed from a sphere.While DIB-R also supports prediction of lighting, its performance maynot be sufficient for realistic imagery, so lighting estimation isomitted in the present discussion.

To train the network, a renderer such as DIB-R can be adopted as thedifferentiable graphics renderer that takes {S, T} and Vas input andproduces a rendered image I′_(V)=r(S, T, V) along with a rendered maskM′. Following DIB-R, the loss function then takes the following form:

L(I, S, T, V; θ) = λ_(col)L_(col)(I, I^(′)) + λ_(percept)L_(percept)(I, I^(′)) + L_(IOU)(M, M^(′)) + λ_(sm)L_(sm)(S) + λ_(lap)L_(lap)(S) + λ_(mov)L_(mov)(S)Here, L_(col) is the standard L₁ image reconstruction loss defined inthe RGB color space while L_(percept) is the perceptual loss that helpsthe predicted texture look more realistic. Note that rendered images donot have background, so L_(col) and L_(percept) are calculated byutilizing the mask. L_(IOU) computes the intersection-over-union betweenthe ground-truth mask and the rendered mask. Regularization losses suchas the Laplacian loss L_(lap) and flatten loss L_(sm) are commonly usedto ensure that the shape is well behaved. Finally, L_(mov) regularizesthe shape deformation to be uniform and small.

Since there may also be access to multi-view images for each object, amulti-view consistency loss can be included. In particular, the loss perobject k can be given by:

${L_{k}(\theta)} = {\sum\limits_{i,j,{i \neq j}}\left( {{L\left( {l_{V_{i}^{k}},S_{k},T_{k},V_{i}^{k},\theta} \right)} + {L\left( {I_{V_{i^{\prime}}^{k}},S_{k},T_{k},V_{j}^{k},\theta} \right)}} \right)}$

-   -   where {S_(k), T_(k), L_(k)}=f_(θ) (I_(v) _(i) ^(k))        While more views provide more constraints, empirically, two        views have been proven sufficient. View pairs (i,j) can be        randomly sampled for efficiency. The above loss functions can be        used to jointly train the neural network f and optimize        viewpoint cameras V. It may be assumed that different images        generated from the same w_(ν)* correspond to the same        viewpoint V. Optimizing the camera jointly with the weights of        the network allows such an approach to effectively deal with        noisy initial camera annotations.

The inverse graphics model allows for the inferring of a 3D mesh andtexture from a given image. These 3D properties can then be used todisentangle the latent space of the generator, and turn the generatorinto a fully controllable 3D neural renderer, such as may be referred toas StyleGAN-R. It can be noted that StyleGAN in fact synthesizes morethan just an object, it also produces the background to result in anentire scene. Approaches in at least some embodiments can also providecontrol over the background as well, allowing the neural renderer torender 3D objects into desired scenes. To get the background from agiven image, the object can be masked out in at least one embodiment.

A mapping network can be trained and used to map the viewpoint, shape(e.g., mesh), texture, and background into the latent code of thegenerator. Since the generator may not be completely disentangled, theentire generative model can be fine-tuned while keeping the inversegraphics network fixed. A mapping network, such as the exampleillustrated in FIG. 3, can map the viewpoints to the first four layersand map the shape, texture, and background to the last twelve layers ofW*. For simplicity, the first four layers can be denoted as WV*, and thelast twelve layers as WS*TB, where WV*∈R2048 and WS*TB∈ R3008. It shouldbe noted that there may be different numbers of layers in other modelsor networks. In this example, the mapping network gν for viewpoint V andgs for shape S are separate MLPs while gt for texture T and gb forbackground B are CNN layers:z ^(view) =g _(ν)(V;θ _(ν)),z ^(shape) =g _(s)(S;θ _(s)),z ^(txt) =g_(t)(T;θ _(t)),z ^(bck) =g _(b)(B;θ _(b)),where z^(view) ∈R, z^(shape), z^(txt), z^(bck)∈R³⁰⁰⁸ and θ_(ν), θ_(s),θ_(t), θ_(b) are network parameters. The shape, texture, and backgroundcodes can be softly combined into the final latent code as follows:{tilde over (w)} ^(mtb) =s ^(m) ⊙z ^(shape) +z ^(txt) +s ^(b) ⊙z ^(bck),where ⊙ denotes element-wise product, and s^(m), s^(t), s^(b)∈R³⁰⁰⁸ areshared across all the samples. To achieve disentanglement, eachdimension of the final code can be explained by only one property (e.g.,shape, texture, or background). A process in accordance with at leastone embodiment can thus normalize each dimension of s using a SoftMax.In practice, it was determined that mapping V to a high dimensional codemay be challenging since a dataset may only contain a limited number ofviews, and V may be limited to azimuth, elevation, and scale. Oneapproach is to map V to the subset of W_(V)*, where the approachempirically chooses a number, such as 144 of the 2048, or dimensionswith the highest correlation with the annotated viewpoints. Thus,z^(view) ∈R¹⁴⁴∈R144 in this example.

In at least one embodiment, the mapping network can be trained and theStyleGAN model fine-tuned in two separate stages. In one example, theStyleGAN model's weights are frozen and only the mapping network istrained. In one or more embodiments, this facilitates or improves theability of the mapping network to output reasonable latent codes for theStyleGAN model. The process can then fine-tune both the StyleGAN modeland the mapping network to better disentangle different attributes. In awarmup stage, viewpoint codes w_(ν)* can be sampled among the chosenviewpoints, and the remaining dimensions of w*∈W* sampled. An attemptcan be made to minimize the L₂ difference between the mapped code {tildeover (w)} and StyleGAN model's code w*. To encourage the disentanglementin the latent space, the entropy of each dimension i of s can bepenalized. An example overall loss function for this mapping network canthen be given by:

${L_{mapnet}\left( {\theta_{v},\theta_{s},\theta_{t},\theta_{v}} \right)} = {{{\overset{\sim}{w} - w^{*}}}_{2} - {\sum\limits_{i}{\sum\limits_{k \in {\{{m,t,b}\}}}{s_{i}^{k}{\log\left( s_{i}^{k} \right)}}}}}$

By training the mapping network, that view, shape and texture can bedisentangled in the original StyleGAN model, but the background mayremain entangled. The model can thus be fine-tuned to achieve a betterdisentanglement. A cycle consistency loss can be incorporated in orderto fine-tune the StyleGAN model. In particular, by feeding a sampledshape, texture and background to the StyleGAN model a synthesized imagecan be obtained. Such an approach can encourage consistency between theoriginal sampled properties and the shape, texture, and backgroundpredicted from the StyleGAN-synthesized image via the inverse graphicsnetwork. The same background B can be fed with two different {S, T}pairs to generate two images I₁ and I₂. The re-synthesized backgroundsB₁ and B₂ can then be encouraged to be similar. This loss tries todisentangle the background from the foreground object. During training,imposing the consistency loss on B in image space may result in blurryimages, thus it can be constrained in the code space. An examplefine-tuning loss takes the following form:L _(stylegan)(θ_(gan) =∥S−S∥ ₂ +∥T−T∥ ₂ +∥g _(b)(B)−g _(b)( B )∥₂ +∥g_(b)( B ₁)−g _(b) B ₂∥₂)

In one example, a DIB-R based inverse graphics model was trained withAdam, with a learning rate of 1e⁴, setting λ_(IOU), λ_(col), λ_(lap),λ_(sm) and λ_(mov) to 3, 20, 5, 5, and 2.5, respectively. The model wasfirst trained with L_(col) loss for 3K iterations, and then fine-tunedby adding L_(pecept) to make the texture more realistic. The process setA percept to 0.5. The model converged in 200K iterations with batch size16. Training took around 120 hours on four V100 GPUs. The trainingresulted in high quality 3D reconstruction results, including quality ofthe predicted shapes and textures, and the diversity of the 3D shapesobtained. This method also worked well on more challenging (e.g.,articulated) classes, such as animals.

In at least one embodiment, a StyleGAN-R model was trained using Adamwith a learning rate of 1e⁵ and batch size 16. Warmup stage took 700iterations, and joint fine-tuning performed for another 2500 iterations.With the provided input image, the process first predicted mesh andtexture using the trained inverse graphics model, and then fed these 3Dproperties into the StyleGAN-R to generate a new image. For comparison,the same 3D properties were fed to the DIB-R graphics renderer (which isthe OpenGL renderer). It can be noted that DIB-R can only render thepredicted object, while StyleGAN-R also has the ability to render theobject into a desired background. It was found that StyleGAN-R producesrelatively consistent images compared to the input image. Shape andtexture are well preserved, while only the background has a slightcontent shift.

Such an approach was tested in manipulating StyleGAN-synthesized imagesfrom test set and real images. Specifically, given an input image, theapproach predicted 3D properties using the inverse graphics network, andextracted background by masking out the object with a Mask-RCNN. Theapproach then manipulated and fed these properties to a StyleGAN-R tosynthesize new views.

In order to control viewpoint, the process first froze shape, texture,and background, and changed only the camera viewpoint. Meaningfulresults were obtained, particularly for shape and texture. Forcomparison, an alternative way that has been explored is to directlyoptimize the GAN's latent code (in an example case the originalStyleGAN's code) via an L2 image reconstruction loss. Such an approachmay fail to generate plausible images, however, showcasing importance ofthe mapping network and fine-tuning the entire architecture with 3Dinverse graphics network in the loop in at least some embodiments.

To control shape, texture, and background, such an approach can attemptto manipulate these or other 3D properties, while keeping the cameraviewpoint fixed. In one example, the shapes of all cars to can bechanged to one chosen shape and perform neural rendering performed usinga StyleGAN-R. Such a process successfully swapped the shape of the carwhile maintaining other properties. The process is also able to modifytiny parts of the car, such as the trunk and headlights. The sameexperiment can be performed, but swapping texture and background.Swapping textures may also slightly modify the background in someembodiments, pointing that further improvements may be pursued indisentangling the two. Such a framework can also work well when providedwith real images, since StyleGAN's images are quite realistic.

The StyleGAN code repository provides models of different objectcategories at different resolutions. Here a 512×384 car model is takenas an example. This model contains 16 layers, where every twoconsecutive layers form a block. Each block has a different number ofchannels. In the last block, the model produces a 32-channel feature mapat a 512×384 resolution. Finally, a learned RGB transformation functionis applied to convert the feature map into an RGB image. The feature mapfor each block can be visualized via the learned RGB transformationfunction. Specifically, for the feature map in each block with the sizeof h×w×c, the process can first sum along the feature dimension, forminga h×w×1 tensor. The process can repeat the feature 32 times and generatea h×w×32 new feature map. This allows keeping the information of all thechannels and directly applying the RGB transformation function in thelast block to convert it to the RGB image. In this example, blocks 1 and2 do not exhibit interpretable structure while the car shape starts toappear in blocks 3-5. There is a rough car contour in block 4 whichfurther becomes clear in block 5. From blocks 6 to 8, the car's shapebecomes increasingly finer and background scene also appears. Thissupports that the viewpoint is controlled in block 1 and 2 (e.g., thefirst 4 layers) while shape, texture, and background exist in the last12 layers in this example. There was high consistency of both the carshape and texture as well as the background scene across the differentviewpoints. Note that for articulated objects such as horse and birdclasses, a StyleGAN model may not perfectly preserve object articulationin different viewpoints, which may lead to challenges in training highaccuracy models using multi-view consistency loss.

As mentioned, inverse graphics tasks require camera pose informationduring training, which can be challenging to acquire for real imagery.Pose is generally obtained by annotating key points for each object andrunning structure-from-motion (SFM) techniques to compute cameraparameters. However, key point annotation is quite timeconsuming—requiring roughly 3-5 minutes per object in one experiment. AStyleGAN model can be utilized to significantly reduce annotation effortsince samples with the same w_(ν)* share the same viewpoint. Therefore,the process only needs to assign a few selected w_(ν)* into cameraposes. In particular, poses can be assigned into several bins which issufficient for training inverse graphics networks where, along with thenetwork parameters, cameras get jointly optimized during training usingthese bins as initialization. In one example, each view is annotatedwith a rough absolute camera pose (which can be further optimized duringtraining). To be specific, one example can first select 12 azimuthangles: [0°, 30°, 60°, 90°, 120°, 150°, 180°, 210°, 240°, 270°, 300°,330]. Given a StyleGAN viewpoint, the process can include manuallyclassifying which azimuth angle it is close to and assigning it to thecorresponding label with fixed elevation (0°) and camera distance.

To demonstrate the effectiveness of this camera initialization, acomparison can be made with another inverse graphics network trainedwith a more accurate camera initialization. Such an initialization isdone by manually annotating object key points in each of the selectedviews (w_(ν)*) of a single car example, which takes about 3-4 hours(around 200 minutes, 39 views). Note that this is still a significantlylower annotation effort compared to 200-350 hours required to annotatekey points for every single object in the Pascal3D dataset. The cameraparameters can then be computed using Structure from Motion (SfM).Reference can be made to the two inverse graphics networks trained withdifferent camera initializations as view-model and key point-model,respectively. While it takes the same amount of time to train,view-model can save on annotation time. The performance of view-modeland key point-model are comparable with almost the same 2D IOUre-projection score on the StyleGAN test set. Moreover, during trainingthe two camera systems converge to the same position. This can beevaluated by converting all the views into quaternions and compare thedifference between the rotation axes and rotation angles. Among allviews, the average difference of the rotation axes is only 1.43 and therotation angle is 0.42°. The maximum difference of the rotation axes isonly 2.95 and the rotation angle is 1.11. Both qualitative andquantitative comparisons demonstrated that view-camera initialization issufficient for training accurate inverse graphics networks and noadditional annotation is required. This demonstrates a scalable way forcreating multi-view datasets with StyleGAN, with roughly a minute ofannotation time per class.

FIG. 5 illustrates an example process 500 for training an inversegraphics network that can be utilized in accordance with variousembodiments. It should be understood that for this and other processespresented herein that there can be additional, fewer, or alternativesteps performed in similar or alternative order, or at least partiallyin parallel, within scope of various embodiments unless otherwisespecifically stated. Further, although discussed with respect tostreaming video content, it should be understood that such enhancementscan be provided to individual images or image sequences, stored videofiles, augmented or virtual reality streams, or other such content. Inthis example, a two-dimensional (2D) training image is received thatincludes a representation of an object. A set of camera poses is alsoreceived 504, or otherwise determined. Using the 2D image informationwith a generator network, such as a StyleGAN, a set of view images isgenerated including representations of the object with views accordingto the set of camera poses. Here, each generated image would include, orbe associated with, the corresponding camera pose information.

In this example, the set of generated view images can then be provided508 as input to an inverse graphics network. A set of three-dimensional(3D) information, such as may include shape, lighting, and texture, isinferred 510 for the object in the image. One or more representations ofthe object can then be rendered 512 using the 3D information and thecamera information from the input image with a differentiable renderer.The rendered representation(s) can then be compared 514 against thecorresponding ground truth data to determine one or more loss values.One or more network parameters or weights can then be adjusted 516 toattempt to minimize the loss. A determination can be made 518 as towhether an end condition has been satisfied, such as the networkconverging, a maximum number of training passes being reached, or alltraining data being processed, among other such options. If not, thenthe process can continue with the next 2D training image. If an endcriterion has been satisfied, then the optimized network parameters canbe provided 520 for use in inferencing. At least some of the renderedoutput from the inverse graphics network can also be provided 524 astraining data for further training, or fine-tuning, the generatornetwork. In some embodiments, the generator network and inverse graphicsnetwork can be trained together using a common loss function.

As an example, FIG. 6 illustrates a network configuration 600 that canbe used to provide or enhance content. In at least one embodiment, aclient device 602 can generate content for a session using components ofa content application 604 on client device 602 and data stored locallyon that client device. In at least one embodiment, a content application624 (e.g., an image generation or editing application) executing oncontent server 620 (e.g., a cloud server or edge server) may initiate asession associated with at least client device 602, as may utilize asession manager and user data stored in a user database 634, and cancause content 632 to be determined by a content manager 626 and renderedusing a rendering engine, if needed for this type of content orplatform, and transmitted to client device 602 using an appropriatetransmission manager 622 to send by download, streaming, or another suchtransmission channel. In at least one embodiment, this content 632 caninclude 2D or 3D assets that can be used by a rendering engine to rendera scene based on a determined scene graph. In at least one embodiment,client device 602 receiving this content can provide this content to acorresponding content application 604, which may also or alternativelyinclude a rendering engine (if necessary) for rendering at least some ofthis content for presentation via client device 602, such as image orvideo content through a display 606 and audio, such as sounds and music,through at least one audio playback device 608, such as speakers orheadphones. For live video content captured by one or more cameras, forexample, such a rendering engine may not be needed, unless used toaugment that video content in some way. In at least one embodiment, atleast some of this content may already be stored on, rendered on, oraccessible to client device 602 such that transmission over network 640is not required for at least that portion of content, such as where thatcontent may have been previously downloaded or stored locally on a harddrive or optical disk. In at least one embodiment, a transmissionmechanism such as data streaming can be used to transfer this contentfrom server 620, or content database 634, to client device 602. In atleast one embodiment, at least a portion of this content can be obtainedor streamed from another source, such as a third party content service660 that may also include a content application 662 for generating orproviding content. In at least one embodiment, portions of thisfunctionality can be performed using multiple computing devices, ormultiple processors within one or more computing devices, such as mayinclude a combination of CPUs and GPUs.

In at least one embodiment, content application 624 includes a contentmanager 626 that can determine or analyze content before this content istransmitted to client device 602. In at least one embodiment, contentmanager 626 can also include, or work with, other components that areable to generate, modify, or enhance content to be provided. In at leastone embodiment, this can include a rendering engine for rendering imageor video content. This rendering engine may be part of an inversegraphics network in at least some embodiments. In at least oneembodiment, an image, video, or scene generation component 628 can beused to generate image, video, or other media content. In at least oneembodiment, an inverse graphics component 630, which can also include aneural network, can generate representations based on inferred 3Dinformation, as discussed and suggested herein. In at least oneembodiment, content manager 626 can cause this content (enhanced or not)to be transmitted to client device 602. In at least one embodiment, acontent application 604 on client device 602 may also include componentssuch as a rendering engine, image or video generator 612, and inversegraphics module 614, such that any or all of this functionality canadditionally, or alternatively, be performed on client device 602. In atleast one embodiment, a content application 662 on a third party contentservice system 660 can also include such functionality. In at least oneembodiment, locations where at least some of this functionality isperformed may be configurable, or may depend upon factors such as a typeof client device 602 or availability of a network connection withappropriate bandwidth, among other such factors. In at least oneembodiment, a system for content generation can include any appropriatecombination of hardware and software in one or more locations. In atleast one embodiment, generated image or video content of one or moreresolutions can also be provided, or made available, to other clientdevices 650, such as for download or streaming from a media sourcestoring a copy of that image or video content. In at least oneembodiment, this may include transmitting images of game content for amultiplayer game, where different client devices may display thatcontent at different resolutions, including one or moresuper-resolutions.

In this example, these client devices can include any appropriatecomputing devices, as may include a desktop computer, notebook computer,set-top box, streaming device, gaming console, smartphone, tabletcomputer, VR headset, AR goggles, wearable computer, or a smarttelevision. Each client device can submit a request across at least onewired or wireless network, as may include the Internet, an Ethernet, alocal area network (LAN), or a cellular network, among other suchoptions. In this example, these requests can be submitted to an addressassociated with a cloud provider, who may operate or control one or moreelectronic resources in a cloud provider environment, such as mayinclude a data center or server farm. In at least one embodiment, therequest may be received or processed by at least one edge server, thatsits on a network edge and is outside at least one security layerassociated with the cloud provider environment. In this way, latency canbe reduced by enabling the client devices to interact with servers thatare in closer proximity, while also improving security of resources inthe cloud provider environment.

In at least one embodiment, such a system can be used for performinggraphical rendering operations. In other embodiments, such a system canbe used for other purposes, such as for providing image or video contentto test or validate autonomous machine applications, or for performingdeep learning operations. In at least one embodiment, such a system canbe implemented using an edge device, or may incorporate one or moreVirtual Machines (VMs). In at least one embodiment, such a system can beimplemented at least partially in a data center or at least partiallyusing cloud computing resources.

Inference and Training Logic

FIG. 7A illustrates inference and/or training logic 715 used to performinferencing and/or training operations associated with one or moreembodiments. Details regarding inference and/or training logic 715 areprovided below in conjunction with FIGS. 7A and/or 7B.

In at least one embodiment, inference and/or training logic 715 mayinclude, without limitation, code and/or data storage 701 to storeforward and/or output weight and/or input/output data, and/or otherparameters to configure neurons or layers of a neural network trainedand/or used for inferencing in aspects of one or more embodiments. In atleast one embodiment, training logic 715 may include, or be coupled tocode and/or data storage 701 to store graph code or other software tocontrol timing and/or order, in which weight and/or other parameterinformation is to be loaded to configure, logic, including integerand/or floating point units (collectively, arithmetic logic units(ALUs). In at least one embodiment, code, such as graph code, loadsweight or other parameter information into processor ALUs based on anarchitecture of a neural network to which the code corresponds. In atleast one embodiment, code and/or data storage 701 stores weightparameters and/or input/output data of each layer of a neural networktrained or used in conjunction with one or more embodiments duringforward propagation of input/output data and/or weight parameters duringtraining and/or inferencing using aspects of one or more embodiments. Inat least one embodiment, any portion of code and/or data storage 701 maybe included with other on-chip or off-chip data storage, including aprocessor's L1, L2, or L3 cache or system memory.

In at least one embodiment, any portion of code and/or data storage 701may be internal or external to one or more processors or other hardwarelogic devices or circuits. In at least one embodiment, code and/or codeand/or data storage 701 may be cache memory, dynamic randomlyaddressable memory (“DRAM”), static randomly addressable memory(“SRAM”), non-volatile memory (e.g., Flash memory), or other storage. Inat least one embodiment, choice of whether code and/or code and/or datastorage 701 is internal or external to a processor, for example, orcomprised of DRAM, SRAM, Flash or some other storage type may depend onavailable storage on-chip versus off-chip, latency requirements oftraining and/or inferencing functions being performed, batch size ofdata used in inferencing and/or training of a neural network, or somecombination of these factors.

In at least one embodiment, inference and/or training logic 715 mayinclude, without limitation, a code and/or data storage 705 to storebackward and/or output weight and/or input/output data corresponding toneurons or layers of a neural network trained and/or used forinferencing in aspects of one or more embodiments. In at least oneembodiment, code and/or data storage 705 stores weight parameters and/orinput/output data of each layer of a neural network trained or used inconjunction with one or more embodiments during backward propagation ofinput/output data and/or weight parameters during training and/orinferencing using aspects of one or more embodiments. In at least oneembodiment, training logic 715 may include, or be coupled to code and/ordata storage 705 to store graph code or other software to control timingand/or order, in which weight and/or other parameter information is tobe loaded to configure, logic, including integer and/or floating pointunits (collectively, arithmetic logic units (ALUs). In at least oneembodiment, code, such as graph code, loads weight or other parameterinformation into processor ALUs based on an architecture of a neuralnetwork to which the code corresponds. In at least one embodiment, anyportion of code and/or data storage 705 may be included with otheron-chip or off-chip data storage, including a processor's L1, L2, or L3cache or system memory. In at least one embodiment, any portion of codeand/or data storage 705 may be internal or external to on one or moreprocessors or other hardware logic devices or circuits. In at least oneembodiment, code and/or data storage 705 may be cache memory, DRAM,SRAM, non-volatile memory (e.g., Flash memory), or other storage. In atleast one embodiment, choice of whether code and/or data storage 705 isinternal or external to a processor, for example, or comprised of DRAM,SRAM, Flash or some other storage type may depend on available storageon-chip versus off-chip, latency requirements of training and/orinferencing functions being performed, batch size of data used ininferencing and/or training of a neural network, or some combination ofthese factors.

In at least one embodiment, code and/or data storage 701 and code and/ordata storage 705 may be separate storage structures. In at least oneembodiment, code and/or data storage 701 and code and/or data storage705 may be same storage structure. In at least one embodiment, codeand/or data storage 701 and code and/or data storage 705 may bepartially same storage structure and partially separate storagestructures. In at least one embodiment, any portion of code and/or datastorage 701 and code and/or data storage 705 may be included with otheron-chip or off-chip data storage, including a processor's L1, L2, or L3cache or system memory.

In at least one embodiment, inference and/or training logic 715 mayinclude, without limitation, one or more arithmetic logic unit(s)(“ALU(s)”) 710, including integer and/or floating point units, toperform logical and/or mathematical operations based, at least in parton, or indicated by, training and/or inference code (e.g., graph code),a result of which may produce activations (e.g., output values fromlayers or neurons within a neural network) stored in an activationstorage 720 that are functions of input/output and/or weight parameterdata stored in code and/or data storage 701 and/or code and/or datastorage 705. In at least one embodiment, activations stored inactivation storage 720 are generated according to linear algebraic andor matrix-based mathematics performed by ALU(s) 710 in response toperforming instructions or other code, wherein weight values stored incode and/or data storage 705 and/or code and/or data storage 701 areused as operands along with other values, such as bias values, gradientinformation, momentum values, or other parameters or hyperparameters,any or all of which may be stored in code and/or data storage 705 orcode and/or data storage 701 or another storage on or off-chip.

In at least one embodiment, ALU(s) 710 are included within one or moreprocessors or other hardware logic devices or circuits, whereas inanother embodiment, ALU(s) 710 may be external to a processor or otherhardware logic device or circuit that uses them (e.g., a co-processor).In at least one embodiment, ALUs 710 may be included within aprocessor's execution units or otherwise within a bank of ALUsaccessible by a processor's execution units either within same processoror distributed between different processors of different types (e.g.,central processing units, graphics processing units, fixed functionunits, etc.). In at least one embodiment, code and/or data storage 701,code and/or data storage 705, and activation storage 720 may be on sameprocessor or other hardware logic device or circuit, whereas in anotherembodiment, they may be in different processors or other hardware logicdevices or circuits, or some combination of same and differentprocessors or other hardware logic devices or circuits. In at least oneembodiment, any portion of activation storage 720 may be included withother on-chip or off-chip data storage, including a processor's L1, L2,or L3 cache or system memory. Furthermore, inferencing and/or trainingcode may be stored with other code accessible to a processor or otherhardware logic or circuit and fetched and/or processed using aprocessor's fetch, decode, scheduling, execution, retirement and/orother logical circuits.

In at least one embodiment, activation storage 720 may be cache memory,DRAM, SRAM, non-volatile memory (e.g., Flash memory), or other storage.In at least one embodiment, activation storage 720 may be completely orpartially within or external to one or more processors or other logicalcircuits. In at least one embodiment, choice of whether activationstorage 720 is internal or external to a processor, for example, orcomprised of DRAM, SRAM, Flash or some other storage type may depend onavailable storage on-chip versus off-chip, latency requirements oftraining and/or inferencing functions being performed, batch size ofdata used in inferencing and/or training of a neural network, or somecombination of these factors. In at least one embodiment, inferenceand/or training logic 715 illustrated in FIG. 7a may be used inconjunction with an application-specific integrated circuit (“ASIC”),such as Tensorflow® Processing Unit from Google, an inference processingunit (IPU) from Graphcore™, or a Nervana® (e.g., “Lake Crest”) processorfrom Intel Corp. In at least one embodiment, inference and/or traininglogic 715 illustrated in FIG. 7a may be used in conjunction with centralprocessing unit (“CPU”) hardware, graphics processing unit (“GPU”)hardware or other hardware, such as field programmable gate arrays(“FPGAs”).

FIG. 7b illustrates inference and/or training logic 715, according to atleast one or more embodiments. In at least one embodiment, inferenceand/or training logic 715 may include, without limitation, hardwarelogic in which computational resources are dedicated or otherwiseexclusively used in conjunction with weight values or other informationcorresponding to one or more layers of neurons within a neural network.In at least one embodiment, inference and/or training logic 715illustrated in FIG. 7b may be used in conjunction with anapplication-specific integrated circuit (ASIC), such as Tensorflow®Processing Unit from Google, an inference processing unit (IPU) fromGraphcore™, or a Nervana® (e.g., “Lake Crest”) processor from IntelCorp. In at least one embodiment, inference and/or training logic 715illustrated in FIG. 7b may be used in conjunction with centralprocessing unit (CPU) hardware, graphics processing unit (GPU) hardwareor other hardware, such as field programmable gate arrays (FPGAs). In atleast one embodiment, inference and/or training logic 715 includes,without limitation, code and/or data storage 701 and code and/or datastorage 705, which may be used to store code (e.g., graph code), weightvalues and/or other information, including bias values, gradientinformation, momentum values, and/or other parameter or hyperparameterinformation. In at least one embodiment illustrated in FIG. 7b , each ofcode and/or data storage 701 and code and/or data storage 705 isassociated with a dedicated computational resource, such ascomputational hardware 702 and computational hardware 706, respectively.In at least one embodiment, each of computational hardware 702 andcomputational hardware 706 comprises one or more ALUs that performmathematical functions, such as linear algebraic functions, only oninformation stored in code and/or data storage 701 and code and/or datastorage 705, respectively, result of which is stored in activationstorage 720.

In at least one embodiment, each of code and/or data storage 701 and 705and corresponding computational hardware 702 and 706, respectively,correspond to different layers of a neural network, such that resultingactivation from one “storage/computational pair 701/702” of code and/ordata storage 701 and computational hardware 702 is provided as an inputto “storage/computational pair 705/706” of code and/or data storage 705and computational hardware 706, in order to mirror conceptualorganization of a neural network. In at least one embodiment, each ofstorage/computational pairs 701/702 and 705/706 may correspond to morethan one neural network layer. In at least one embodiment, additionalstorage/computation pairs (not shown) subsequent to or in parallel withstorage computation pairs 701/702 and 705/706 may be included ininference and/or training logic 715.

Data Center

FIG. 8 illustrates an example data center 800, in which at least oneembodiment may be used. In at least one embodiment, data center 800includes a data center infrastructure layer 810, a framework layer 820,a software layer 830, and an application layer 840.

In at least one embodiment, as shown in FIG. 8, data centerinfrastructure layer 810 may include a resource orchestrator 812,grouped computing resources 814, and node computing resources (“nodeC.R.s”) 816(1)-816(N), where “N” represents any whole, positive integer.In at least one embodiment, node C.R.s 816(1)-816(N) may include, butare not limited to, any number of central processing units (“CPUs”) orother processors (including accelerators, field programmable gate arrays(FPGAs), graphics processors, etc.), memory devices (e.g., dynamicread-only memory), storage devices (e.g., solid state or disk drives),network input/output (“NW I/O”) devices, network switches, virtualmachines (“VMs”), power modules, and cooling modules, etc. In at leastone embodiment, one or more node C.R.s from among node C.R.s816(1)-816(N) may be a server having one or more of above-mentionedcomputing resources.

In at least one embodiment, grouped computing resources 814 may includeseparate groupings of node C.R.s housed within one or more racks (notshown), or many racks housed in data centers at various geographicallocations (also not shown). Separate groupings of node C.R.s withingrouped computing resources 814 may include grouped compute, network,memory or storage resources that may be configured or allocated tosupport one or more workloads. In at least one embodiment, several nodeC.R.s including CPUs or processors may grouped within one or more racksto provide compute resources to support one or more workloads. In atleast one embodiment, one or more racks may also include any number ofpower modules, cooling modules, and network switches, in anycombination.

In at least one embodiment, resource orchestrator 812 may configure orotherwise control one or more node C.R.s 816(1)-816(N) and/or groupedcomputing resources 814. In at least one embodiment, resourceorchestrator 812 may include a software design infrastructure (“SDI”)management entity for data center 800. In at least one embodiment,resource orchestrator may include hardware, software or some combinationthereof.

In at least one embodiment, as shown in FIG. 8, framework layer 820includes a job scheduler 822, a configuration manager 824, a resourcemanager 826 and a distributed file system 828. In at least oneembodiment, framework layer 820 may include a framework to supportsoftware 832 of software layer 830 and/or one or more application(s) 842of application layer 840. In at least one embodiment, software 832 orapplication(s) 842 may respectively include web-based service softwareor applications, such as those provided by Amazon Web Services, GoogleCloud and Microsoft Azure. In at least one embodiment, framework layer820 may be, but is not limited to, a type of free and open-sourcesoftware web application framework such as Apache Spark™ (hereinafter“Spark”) that may utilize distributed file system 828 for large-scaledata processing (e.g., “big data”). In at least one embodiment, jobscheduler 822 may include a Spark driver to facilitate scheduling ofworkloads supported by various layers of data center 800. In at leastone embodiment, configuration manager 824 may be capable of configuringdifferent layers such as software layer 830 and framework layer 820including Spark and distributed file system 828 for supportinglarge-scale data processing. In at least one embodiment, resourcemanager 826 may be capable of managing clustered or grouped computingresources mapped to or allocated for support of distributed file system828 and job scheduler 822. In at least one embodiment, clustered orgrouped computing resources may include grouped computing resource 814at data center infrastructure layer 810. In at least one embodiment,resource manager 826 may coordinate with resource orchestrator 812 tomanage these mapped or allocated computing resources.

In at least one embodiment, software 832 included in software layer 830may include software used by at least portions of node C.R.s816(1)-816(N), grouped computing resources 814, and/or distributed filesystem 828 of framework layer 820. The one or more types of software mayinclude, but are not limited to, Internet web page search software,e-mail virus scan software, database software, and streaming videocontent software.

In at least one embodiment, application(s) 842 included in applicationlayer 840 may include one or more types of applications used by at leastportions of node C.R.s 816(1)-816(N), grouped computing resources 814,and/or distributed file system 828 of framework layer 820. One or moretypes of applications may include, but are not limited to, any number ofa genomics application, a cognitive compute, and a machine learningapplication, including training or inferencing software, machinelearning framework software (e.g., PyTorch, TensorFlow, Caffe, etc.) orother machine learning applications used in conjunction with one or moreembodiments.

In at least one embodiment, any of configuration manager 824, resourcemanager 826, and resource orchestrator 812 may implement any number andtype of self-modifying actions based on any amount and type of dataacquired in any technically feasible fashion. In at least oneembodiment, self-modifying actions may relieve a data center operator ofdata center 800 from making possibly bad configuration decisions andpossibly avoiding underutilized and/or poor performing portions of adata center.

In at least one embodiment, data center 800 may include tools, services,software or other resources to train one or more machine learning modelsor predict or infer information using one or more machine learningmodels according to one or more embodiments described herein. Forexample, in at least one embodiment, a machine learning model may betrained by calculating weight parameters according to a neural networkarchitecture using software and computing resources described above withrespect to data center 800. In at least one embodiment, trained machinelearning models corresponding to one or more neural networks may be usedto infer or predict information using resources described above withrespect to data center 800 by using weight parameters calculated throughone or more training techniques described herein.

In at least one embodiment, data center may use CPUs,application-specific integrated circuits (ASICs), GPUs, FPGAs, or otherhardware to perform training and/or inferencing using above-describedresources. Moreover, one or more software and/or hardware resourcesdescribed above may be configured as a service to allow users to trainor performing inferencing of information, such as image recognition,speech recognition, or other artificial intelligence services.

Inference and/or training logic 715 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 715 are provided belowin conjunction with FIGS. 7A and/or 7B. In at least one embodiment,inference and/or training logic 715 may be used in system FIG. 8 forinferencing or predicting operations based, at least in part, on weightparameters calculated using neural network training operations, neuralnetwork functions and/or architectures, or neural network use casesdescribed herein.

Such components can be used to train an inverse graphics network using aset of images generated by a generator network, where aspects of objectsare kept fixed while pose or view information is varied between imagesof the set.

Computer Systems

FIG. 9 is a block diagram illustrating an exemplary computer system,which may be a system with interconnected devices and components, asystem-on-a-chip (SOC) or some combination thereof 900 formed with aprocessor that may include execution units to execute an instruction,according to at least one embodiment. In at least one embodiment,computer system 900 may include, without limitation, a component, suchas a processor 902 to employ execution units including logic to performalgorithms for process data, in accordance with present disclosure, suchas in embodiment described herein. In at least one embodiment, computersystem 900 may include processors, such as PENTIUM® Processor family,Xeon™, Itanium®, XScale™ and/or StrongARM™, Intel® Core™, or Intel®Nervana™ microprocessors available from Intel Corporation of SantaClara, Calif., although other systems (including PCs having othermicroprocessors, engineering workstations, set-top boxes and like) mayalso be used. In at least one embodiment, computer system 900 mayexecute a version of WINDOWS' operating system available from MicrosoftCorporation of Redmond, Wash., although other operating systems (UNIXand Linux for example), embedded software, and/or graphical userinterfaces, may also be used.

Embodiments may be used in other devices such as handheld devices andembedded applications. Some examples of handheld devices includecellular phones, Internet Protocol devices, digital cameras, personaldigital assistants (“PDAs”), and handheld PCs. In at least oneembodiment, embedded applications may include a microcontroller, adigital signal processor (“DSP”), system on a chip, network computers(“NetPCs”), set-top boxes, network hubs, wide area network (“WAN”)switches, or any other system that may perform one or more instructionsin accordance with at least one embodiment.

In at least one embodiment, computer system 900 may include, withoutlimitation, processor 902 that may include, without limitation, one ormore execution units 908 to perform machine learning model trainingand/or inferencing according to techniques described herein. In at leastone embodiment, computer system 900 is a single processor desktop orserver system, but in another embodiment computer system 900 may be amultiprocessor system. In at least one embodiment, processor 902 mayinclude, without limitation, a complex instruction set computer (“CISC”)microprocessor, a reduced instruction set computing (“RISC”)microprocessor, a very long instruction word (“VLIW”) microprocessor, aprocessor implementing a combination of instruction sets, or any otherprocessor device, such as a digital signal processor, for example. In atleast one embodiment, processor 902 may be coupled to a processor bus910 that may transmit data signals between processor 902 and othercomponents in computer system 900.

In at least one embodiment, processor 902 may include, withoutlimitation, a Level 1 (“L1”) internal cache memory (“cache”) 904. In atleast one embodiment, processor 902 may have a single internal cache ormultiple levels of internal cache. In at least one embodiment, cachememory may reside external to processor 902. Other embodiments may alsoinclude a combination of both internal and external caches depending onparticular implementation and needs. In at least one embodiment,register file 906 may store different types of data in various registersincluding, without limitation, integer registers, floating pointregisters, status registers, and instruction pointer register.

In at least one embodiment, execution unit 908, including, withoutlimitation, logic to perform integer and floating point operations, alsoresides in processor 902. In at least one embodiment, processor 902 mayalso include a microcode (“ucode”) read only memory (“ROM”) that storesmicrocode for certain macro instructions. In at least one embodiment,execution unit 908 may include logic to handle a packed instruction set909. In at least one embodiment, by including packed instruction set 909in an instruction set of a general-purpose processor 902, along withassociated circuitry to execute instructions, operations used by manymultimedia applications may be performed using packed data in ageneral-purpose processor 902. In one or more embodiments, manymultimedia applications may be accelerated and executed more efficientlyby using full width of a processor's data bus for performing operationson packed data, which may eliminate need to transfer smaller units ofdata across processor's data bus to perform one or more operations onedata element at a time.

In at least one embodiment, execution unit 908 may also be used inmicrocontrollers, embedded processors, graphics devices, DSPs, and othertypes of logic circuits. In at least one embodiment, computer system 900may include, without limitation, a memory 920. In at least oneembodiment, memory 920 may be implemented as a Dynamic Random AccessMemory (“DRAM”) device, a Static Random Access Memory (“SRAM”) device,flash memory device, or other memory device. In at least one embodiment,memory 920 may store instruction(s) 919 and/or data 921 represented bydata signals that may be executed by processor 902.

In at least one embodiment, system logic chip may be coupled toprocessor bus 910 and memory 920. In at least one embodiment, systemlogic chip may include, without limitation, a memory controller hub(“MCH”) 916, and processor 902 may communicate with MCH 916 viaprocessor bus 910. In at least one embodiment, MCH 916 may provide ahigh bandwidth memory path 918 to memory 920 for instruction and datastorage and for storage of graphics commands, data and textures. In atleast one embodiment, MCH 916 may direct data signals between processor902, memory 920, and other components in computer system 900 and tobridge data signals between processor bus 910, memory 920, and a systemI/O 922. In at least one embodiment, system logic chip may provide agraphics port for coupling to a graphics controller. In at least oneembodiment, MCH 916 may be coupled to memory 920 through a highbandwidth memory path 918 and graphics/video card 912 may be coupled toMCH 916 through an Accelerated Graphics Port (“AGP”) interconnect 914.

In at least one embodiment, computer system 900 may use system I/O 922that is a proprietary hub interface bus to couple MCH 916 to I/Ocontroller hub (“ICH”) 930. In at least one embodiment, ICH 930 mayprovide direct connections to some I/O devices via a local I/O bus. Inat least one embodiment, local I/O bus may include, without limitation,a high-speed I/O bus for connecting peripherals to memory 920, chipset,and processor 902. Examples may include, without limitation, an audiocontroller 929, a firmware hub (“flash BIOS”) 928, a wirelesstransceiver 926, a data storage 924, a legacy I/O controller 923containing user input and keyboard interfaces 925, a serial expansionport 927, such as Universal Serial Bus (“USB”), and a network controller934. Data storage 924 may comprise a hard disk drive, a floppy diskdrive, a CD-ROM device, a flash memory device, or other mass storagedevice.

In at least one embodiment, FIG. 9 illustrates a system, which includesinterconnected hardware devices or “chips”, whereas in otherembodiments, FIG. 9 may illustrate an exemplary System on a Chip(“SoC”). In at least one embodiment, devices may be interconnected withproprietary interconnects, standardized interconnects (e.g., PCIe) orsome combination thereof. In at least one embodiment, one or morecomponents of computer system 900 are interconnected using computeexpress link (CXL) interconnects.

Inference and/or training logic 715 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 715 are provided belowin conjunction with FIGS. 7A and/or 7B. In at least one embodiment,inference and/or training logic 715 may be used in system FIG. 9 forinferencing or predicting operations based, at least in part, on weightparameters calculated using neural network training operations, neuralnetwork functions and/or architectures, or neural network use casesdescribed herein.

Such components can be used to train an inverse graphics network using aset of images generated by a generator network, where aspects of objectsare kept fixed while pose or view information is varied between imagesof the set.

FIG. 10 is a block diagram illustrating an electronic device 1000 forutilizing a processor 1010, according to at least one embodiment. In atleast one embodiment, electronic device 1000 may be, for example andwithout limitation, a notebook, a tower server, a rack server, a bladeserver, a laptop, a desktop, a tablet, a mobile device, a phone, anembedded computer, or any other suitable electronic device.

In at least one embodiment, system 1000 may include, without limitation,processor 1010 communicatively coupled to any suitable number or kind ofcomponents, peripherals, modules, or devices. In at least oneembodiment, processor 1010 coupled using a bus or interface, such as a1° C. bus, a System Management Bus (“SMBus”), a Low Pin Count (LPC) bus,a Serial Peripheral Interface (“SPI”), a High Definition Audio (“HDA”)bus, a Serial Advance Technology Attachment (“SATA”) bus, a UniversalSerial Bus (“USB”) (versions 1, 2, 3), or a Universal AsynchronousReceiver/Transmitter (“UART”) bus. In at least one embodiment, FIG. 10illustrates a system, which includes interconnected hardware devices or“chips”, whereas in other embodiments, FIG. 10 may illustrate anexemplary System on a Chip (“SoC”). In at least one embodiment, devicesillustrated in FIG. 10 may be interconnected with proprietaryinterconnects, standardized interconnects (e.g., PCIe) or somecombination thereof. In at least one embodiment, one or more componentsof FIG. 10 are interconnected using compute express link (CXL)interconnects.

In at least one embodiment, FIG. 10 may include a display 1024, a touchscreen 1025, a touch pad 1030, a Near Field Communications unit (“NFC”)1045, a sensor hub 1040, a thermal sensor 1046, an Express Chipset(“EC”) 1035, a Trusted Platform Module (“TPM”) 1038, BIOS/firmware/flashmemory (“BIOS, FW Flash”) 1022, a DSP 1060, a drive 1020 such as a SolidState Disk (“SSD”) or a Hard Disk Drive (“HDD”), a wireless local areanetwork unit (“WLAN”) 1050, a Bluetooth unit 1052, a Wireless Wide AreaNetwork unit (“WWAN”) 1056, a Global Positioning System (GPS) 1055, acamera (“USB 3.0 camera”) 1054 such as a USB 3.0 camera, and/or a LowPower Double Data Rate (“LPDDR”) memory unit (“LPDDR3”) 1015 implementedin, for example, LPDDR3 standard. These components may each beimplemented in any suitable manner.

In at least one embodiment, other components may be communicativelycoupled to processor 1010 through components discussed above. In atleast one embodiment, an accelerometer 1041, Ambient Light Sensor(“ALS”) 1042, compass 1043, and a gyroscope 1044 may be communicativelycoupled to sensor hub 1040. In at least one embodiment, thermal sensor1039, a fan 1037, a keyboard 1046, and a touch pad 1030 may becommunicatively coupled to EC 1035. In at least one embodiment, speaker1063, headphones 1064, and microphone (“mic”) 1065 may becommunicatively coupled to an audio unit (“audio codec and class d amp”)1062, which may in turn be communicatively coupled to DSP 1060. In atleast one embodiment, audio unit 1064 may include, for example andwithout limitation, an audio coder/decoder (“codec”) and a class Damplifier. In at least one embodiment, SIM card (“SIM”) 1057 may becommunicatively coupled to WWAN unit 1056. In at least one embodiment,components such as WLAN unit 1050 and Bluetooth unit 1052, as well asWWAN unit 1056 may be implemented in a Next Generation Form Factor(“NGFF”).

Inference and/or training logic 715 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 715 are provided belowin conjunction with FIGS. 7a and/or 7 b. In at least one embodiment,inference and/or training logic 715 may be used in system FIG. 10 forinferencing or predicting operations based, at least in part, on weightparameters calculated using neural network training operations, neuralnetwork functions and/or architectures, or neural network use casesdescribed herein.

Such components can be used to train an inverse graphics network using aset of images generated by a generator network, where aspects of objectsare kept fixed while pose or view information is varied between imagesof the set.

FIG. 11 is a block diagram of a processing system, according to at leastone embodiment. In at least one embodiment, system 1100 includes one ormore processors 1102 and one or more graphics processors 1108, and maybe a single processor desktop system, a multiprocessor workstationsystem, or a server system having a large number of processors 1102 orprocessor cores 1107. In at least one embodiment, system 1100 is aprocessing platform incorporated within a system-on-a-chip (SoC)integrated circuit for use in mobile, handheld, or embedded devices.

In at least one embodiment, system 1100 can include, or be incorporatedwithin a server-based gaming platform, a game console, including a gameand media console, a mobile gaming console, a handheld game console, oran online game console. In at least one embodiment, system 1100 is amobile phone, smart phone, tablet computing device or mobile Internetdevice. In at least one embodiment, processing system 1100 can alsoinclude, couple with, or be integrated within a wearable device, such asa smart watch wearable device, smart eyewear device, augmented realitydevice, or virtual reality device. In at least one embodiment,processing system 1100 is a television or set top box device having oneor more processors 1102 and a graphical interface generated by one ormore graphics processors 1108.

In at least one embodiment, one or more processors 1102 each include oneor more processor cores 1107 to process instructions which, whenexecuted, perform operations for system and user software. In at leastone embodiment, each of one or more processor cores 1107 is configuredto process a specific instruction set 1109. In at least one embodiment,instruction set 1109 may facilitate Complex Instruction Set Computing(CISC), Reduced Instruction Set Computing (RISC), or computing via aVery Long Instruction Word (VLIW). In at least one embodiment, processorcores 1107 may each process a different instruction set 1109, which mayinclude instructions to facilitate emulation of other instruction sets.In at least one embodiment, processor core 1107 may also include otherprocessing devices, such a Digital Signal Processor (DSP).

In at least one embodiment, processor 1102 includes cache memory 1104.In at least one embodiment, processor 1102 can have a single internalcache or multiple levels of internal cache. In at least one embodiment,cache memory is shared among various components of processor 1102. In atleast one embodiment, processor 1102 also uses an external cache (e.g.,a Level-3 (L3) cache or Last Level Cache (LLC)) (not shown), which maybe shared among processor cores 1107 using known cache coherencytechniques. In at least one embodiment, register file 1106 isadditionally included in processor 1102 which may include differenttypes of registers for storing different types of data (e.g., integerregisters, floating point registers, status registers, and aninstruction pointer register). In at least one embodiment, register file1106 may include general-purpose registers or other registers.

In at least one embodiment, one or more processor(s) 1102 are coupledwith one or more interface bus(es) 1110 to transmit communicationsignals such as address, data, or control signals between processor 1102and other components in system 1100. In at least one embodiment,interface bus 1110, in one embodiment, can be a processor bus, such as aversion of a Direct Media Interface (DMI) bus. In at least oneembodiment, interface 1110 is not limited to a DMI bus, and may includeone or more Peripheral Component Interconnect buses (e.g., PCI, PCIExpress), memory busses, or other types of interface busses. In at leastone embodiment processor(s) 1102 include an integrated memory controller1116 and a platform controller hub 1130. In at least one embodiment,memory controller 1116 facilitates communication between a memory deviceand other components of system 1100, while platform controller hub (PCH)1130 provides connections to I/O devices via a local I/O bus.

In at least one embodiment, memory device 1120 can be a dynamic randomaccess memory (DRAM) device, a static random access memory (SRAM)device, flash memory device, phase-change memory device, or some othermemory device having suitable performance to serve as process memory. Inat least one embodiment memory device 1120 can operate as system memoryfor system 1100, to store data 1122 and instructions 1121 for use whenone or more processors 1102 executes an application or process. In atleast one embodiment, memory controller 1116 also couples with anoptional external graphics processor 1112, which may communicate withone or more graphics processors 1108 in processors 1102 to performgraphics and media operations. In at least one embodiment, a displaydevice 1111 can connect to processor(s) 1102. In at least one embodimentdisplay device 1111 can include one or more of an internal displaydevice, as in a mobile electronic device or a laptop device or anexternal display device attached via a display interface (e.g.,DisplayPort, etc.). In at least one embodiment, display device 1111 caninclude a head mounted display (HMD) such as a stereoscopic displaydevice for use in virtual reality (VR) applications or augmented reality(AR) applications.

In at least one embodiment, platform controller hub 1130 enablesperipherals to connect to memory device 1120 and processor 1102 via ahigh-speed I/O bus. In at least one embodiment, I/O peripherals include,but are not limited to, an audio controller 1146, a network controller1134, a firmware interface 1128, a wireless transceiver 1126, touchsensors 1125, a data storage device 1124 (e.g., hard disk drive, flashmemory, etc.). In at least one embodiment, data storage device 1124 canconnect via a storage interface (e.g., SATA) or via a peripheral bus,such as a Peripheral Component Interconnect bus (e.g., PCI, PCIExpress). In at least one embodiment, touch sensors 1125 can includetouch screen sensors, pressure sensors, or fingerprint sensors. In atleast one embodiment, wireless transceiver 1126 can be a Wi-Fitransceiver, a Bluetooth transceiver, or a mobile network transceiversuch as a 3G, 4G, or Long Term Evolution (LTE) transceiver. In at leastone embodiment, firmware interface 1128 enables communication withsystem firmware, and can be, for example, a unified extensible firmwareinterface (UEFI). In at least one embodiment, network controller 1134can enable a network connection to a wired network. In at least oneembodiment, a high-performance network controller (not shown) coupleswith interface bus 1110. In at least one embodiment, audio controller1146 is a multi-channel high definition audio controller. In at leastone embodiment, system 1100 includes an optional legacy I/O controller1140 for coupling legacy (e.g., Personal System 2 (PS/2)) devices tosystem. In at least one embodiment, platform controller hub 1130 canalso connect to one or more Universal Serial Bus (USB) controllers 1142connect input devices, such as keyboard and mouse 1143 combinations, acamera 1144, or other USB input devices.

In at least one embodiment, an instance of memory controller 1116 andplatform controller hub 1130 may be integrated into a discreet externalgraphics processor, such as external graphics processor 1112. In atleast one embodiment, platform controller hub 1130 and/or memorycontroller 1116 may be external to one or more processor(s) 1102. Forexample, in at least one embodiment, system 1100 can include an externalmemory controller 1116 and platform controller hub 1130, which may beconfigured as a memory controller hub and peripheral controller hubwithin a system chipset that is in communication with processor(s) 1102.

Inference and/or training logic 715 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 715 are provided belowin conjunction with FIGS. 7A and/or 7B. In at least one embodimentportions or all of inference and/or training logic 715 may beincorporated into graphics processor 1500. For example, in at least oneembodiment, training and/or inferencing techniques described herein mayuse one or more of ALUs embodied in a graphics processor. Moreover, inat least one embodiment, inferencing and/or training operationsdescribed herein may be done using logic other than logic illustrated inFIG. 7A or 7B. In at least one embodiment, weight parameters may bestored in on-chip or off-chip memory and/or registers (shown or notshown) that configure ALUs of a graphics processor to perform one ormore machine learning algorithms, neural network architectures, usecases, or training techniques described herein.

Such components can be used to train an inverse graphics network using aset of images generated by a generator network, where aspects of objectsare kept fixed while pose or view information is varied between imagesof the set.

FIG. 12 is a block diagram of a processor 1200 having one or moreprocessor cores 1202A-1202N, an integrated memory controller 1214, andan integrated graphics processor 1208, according to at least oneembodiment. In at least one embodiment, processor 1200 can includeadditional cores up to and including additional core 1202N representedby dashed lined boxes. In at least one embodiment, each of processorcores 1202A-1202N includes one or more internal cache units 1204A-1204N.In at least one embodiment, each processor core also has access to oneor more shared cached units 1206.

In at least one embodiment, internal cache units 1204A-1204N and sharedcache units 1206 represent a cache memory hierarchy within processor1200. In at least one embodiment, cache memory units 1204A-1204N mayinclude at least one level of instruction and data cache within eachprocessor core and one or more levels of shared mid-level cache, such asa Level 2 (L2), Level 3 (L3), Level 4 (L4), or other levels of cache,where a highest level of cache before external memory is classified asan LLC. In at least one embodiment, cache coherency logic maintainscoherency between various cache units 1206 and 1204A-1204N.

In at least one embodiment, processor 1200 may also include a set of oneor more bus controller units 1216 and a system agent core 1210. In atleast one embodiment, one or more bus controller units 1216 manage a setof peripheral buses, such as one or more PCI or PCI express busses. Inat least one embodiment, system agent core 1210 provides managementfunctionality for various processor components. In at least oneembodiment, system agent core 1210 includes one or more integratedmemory controllers 1214 to manage access to various external memorydevices (not shown).

In at least one embodiment, one or more of processor cores 1202A-1202Ninclude support for simultaneous multi-threading. In at least oneembodiment, system agent core 1210 includes components for coordinatingand operating cores 1202A-1202N during multi-threaded processing. In atleast one embodiment, system agent core 1210 may additionally include apower control unit (PCU), which includes logic and components toregulate one or more power states of processor cores 1202A-1202N andgraphics processor 1208.

In at least one embodiment, processor 1200 additionally includesgraphics processor 1208 to execute graphics processing operations. In atleast one embodiment, graphics processor 1208 couples with shared cacheunits 1206, and system agent core 1210, including one or more integratedmemory controllers 1214. In at least one embodiment, system agent core1210 also includes a display controller 1211 to drive graphics processoroutput to one or more coupled displays. In at least one embodiment,display controller 1211 may also be a separate module coupled withgraphics processor 1208 via at least one interconnect, or may beintegrated within graphics processor 1208.

In at least one embodiment, a ring based interconnect unit 1212 is usedto couple internal components of processor 1200. In at least oneembodiment, an alternative interconnect unit may be used, such as apoint-to-point interconnect, a switched interconnect, or othertechniques. In at least one embodiment, graphics processor 1208 coupleswith ring interconnect 1212 via an I/O link 1213.

In at least one embodiment, I/O link 1213 represents at least one ofmultiple varieties of I/O interconnects, including an on package I/Ointerconnect which facilitates communication between various processorcomponents and a high-performance embedded memory module 1218, such asan eDRAM module. In at least one embodiment, each of processor cores1202A-1202N and graphics processor 1208 use embedded memory modules 1218as a shared Last Level Cache.

In at least one embodiment, processor cores 1202A-1202N are homogenouscores executing a common instruction set architecture. In at least oneembodiment, processor cores 1202A-1202N are heterogeneous in terms ofinstruction set architecture (ISA), where one or more of processor cores1202A-1202N execute a common instruction set, while one or more othercores of processor cores 1202A-1202N executes a subset of a commoninstruction set or a different instruction set. In at least oneembodiment, processor cores 1202A-1202N are heterogeneous in terms ofmicroarchitecture, where one or more cores having a relatively higherpower consumption couple with one or more power cores having a lowerpower consumption. In at least one embodiment, processor 1200 can beimplemented on one or more chips or as an SoC integrated circuit.

Inference and/or training logic 715 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 715 are provided belowin conjunction with FIGS. 7a and/or 7 b. In at least one embodimentportions or all of inference and/or training logic 715 may beincorporated into processor 1200. For example, in at least oneembodiment, training and/or inferencing techniques described herein mayuse one or more of ALUs embodied in graphics processor 1512, graphicscore(s) 1202A-1202N, or other components in FIG. 12. Moreover, in atleast one embodiment, inferencing and/or training operations describedherein may be done using logic other than logic illustrated in FIG. 7Aor 7B. In at least one embodiment, weight parameters may be stored inon-chip or off-chip memory and/or registers (shown or not shown) thatconfigure ALUs of graphics processor 1200 to perform one or more machinelearning algorithms, neural network architectures, use cases, ortraining techniques described herein.

Such components can be used to train an inverse graphics network using aset of images generated by a generator network, where aspects of objectsare kept fixed while pose or view information is varied between imagesof the set.

Virtualized Computing Platform

FIG. 13 is an example data flow diagram for a process 1300 of generatingand deploying an image processing and inferencing pipeline, inaccordance with at least one embodiment. In at least one embodiment,process 1300 may be deployed for use with imaging devices, processingdevices, and/or other device types at one or more facilities 1302.Process 1300 may be executed within a training system 1304 and/or adeployment system 1306. In at least one embodiment, training system 1304may be used to perform training, deployment, and implementation ofmachine learning models (e.g., neural networks, object detectionalgorithms, computer vision algorithms, etc.) for use in deploymentsystem 1306. In at least one embodiment, deployment system 1306 may beconfigured to offload processing and compute resources among adistributed computing environment to reduce infrastructure requirementsat facility 1302. In at least one embodiment, one or more applicationsin a pipeline may use or call upon services (e.g., inference,visualization, compute, AI, etc.) of deployment system 1306 duringexecution of applications.

In at least one embodiment, some of applications used in advancedprocessing and inferencing pipelines may use machine learning models orother AI to perform one or more processing steps. In at least oneembodiment, machine learning models may be trained at facility 1302using data 1308 (such as imaging data) generated at facility 1302 (andstored on one or more picture archiving and communication system (PACS)servers at facility 1302), may be trained using imaging or sequencingdata 1308 from another facility(ies), or a combination thereof. In atleast one embodiment, training system 1304 may be used to provideapplications, services, and/or other resources for generating working,deployable machine learning models for deployment system 1306.

In at least one embodiment, model registry 1324 may be backed by objectstorage that may support versioning and object metadata. In at least oneembodiment, object storage may be accessible through, for example, acloud storage (e.g., cloud 1426 of FIG. 14) compatible applicationprogramming interface (API) from within a cloud platform. In at leastone embodiment, machine learning models within model registry 1324 mayuploaded, listed, modified, or deleted by developers or partners of asystem interacting with an API. In at least one embodiment, an API mayprovide access to methods that allow users with appropriate credentialsto associate models with applications, such that models may be executedas part of execution of containerized instantiations of applications.

In at least one embodiment, training pipeline 1404 (FIG. 14) may includea scenario where facility 1302 is training their own machine learningmodel, or has an existing machine learning model that needs to beoptimized or updated. In at least one embodiment, imaging data 1308generated by imaging device(s), sequencing devices, and/or other devicetypes may be received. In at least one embodiment, once imaging data1308 is received, AI-assisted annotation 1310 may be used to aid ingenerating annotations corresponding to imaging data 1308 to be used asground truth data for a machine learning model. In at least oneembodiment, AI-assisted annotation 1310 may include one or more machinelearning models (e.g., convolutional neural networks (CNNs)) that may betrained to generate annotations corresponding to certain types ofimaging data 1308 (e.g., from certain devices). In at least oneembodiment, AI-assisted annotations 1310 may then be used directly, ormay be adjusted or fine-tuned using an annotation tool to generateground truth data. In at least one embodiment, AI-assisted annotations1310, labeled clinic data 1312, or a combination thereof may be used asground truth data for training a machine learning model. In at least oneembodiment, a trained machine learning model may be referred to asoutput model 1316, and may be used by deployment system 1306, asdescribed herein.

In at least one embodiment, training pipeline 1404 (FIG. 14) may includea scenario where facility 1302 needs a machine learning model for use inperforming one or more processing tasks for one or more applications indeployment system 1306, but facility 1302 may not currently have such amachine learning model (or may not have a model that is optimized,efficient, or effective for such purposes). In at least one embodiment,an existing machine learning model may be selected from a model registry1324. In at least one embodiment, model registry 1324 may includemachine learning models trained to perform a variety of differentinference tasks on imaging data. In at least one embodiment, machinelearning models in model registry 1324 may have been trained on imagingdata from different facilities than facility 1302 (e.g., facilitiesremotely located). In at least one embodiment, machine learning modelsmay have been trained on imaging data from one location, two locations,or any number of locations. In at least one embodiment, when beingtrained on imaging data from a specific location, training may takeplace at that location, or at least in a manner that protectsconfidentiality of imaging data or restricts imaging data from beingtransferred off-premises. In at least one embodiment, once a model istrained—or partially trained—at one location, a machine learning modelmay be added to model registry 1324. In at least one embodiment, amachine learning model may then be retrained, or updated, at any numberof other facilities, and a retrained or updated model may be madeavailable in model registry 1324. In at least one embodiment, a machinelearning model may then be selected from model registry 1324—andreferred to as output model 1316—and may be used in deployment system1306 to perform one or more processing tasks for one or moreapplications of a deployment system.

In at least one embodiment, training pipeline 1404 (FIG. 14), a scenariomay include facility 1302 requiring a machine learning model for use inperforming one or more processing tasks for one or more applications indeployment system 1306, but facility 1302 may not currently have such amachine learning model (or may not have a model that is optimized,efficient, or effective for such purposes). In at least one embodiment,a machine learning model selected from model registry 1324 may not befine-tuned or optimized for imaging data 1308 generated at facility 1302because of differences in populations, robustness of training data usedto train a machine learning model, diversity in anomalies of trainingdata, and/or other issues with training data. In at least oneembodiment, AI-assisted annotation 1310 may be used to aid in generatingannotations corresponding to imaging data 1308 to be used as groundtruth data for retraining or updating a machine learning model. In atleast one embodiment, labeled data 1312 may be used as ground truth datafor training a machine learning model. In at least one embodiment,retraining or updating a machine learning model may be referred to asmodel training 1314. In at least one embodiment, model training1314—e.g., AI-assisted annotations 1310, labeled clinic data 1312, or acombination thereof—may be used as ground truth data for retraining orupdating a machine learning model. In at least one embodiment, a trainedmachine learning model may be referred to as output model 1316, and maybe used by deployment system 1306, as described herein.

In at least one embodiment, deployment system 1306 may include software1318, services 1320, hardware 1322, and/or other components, features,and functionality. In at least one embodiment, deployment system 1306may include a software “stack,” such that software 1318 may be built ontop of services 1320 and may use services 1320 to perform some or all ofprocessing tasks, and services 1320 and software 1318 may be built ontop of hardware 1322 and use hardware 1322 to execute processing,storage, and/or other compute tasks of deployment system 1306. In atleast one embodiment, software 1318 may include any number of differentcontainers, where each container may execute an instantiation of anapplication. In at least one embodiment, each application may performone or more processing tasks in an advanced processing and inferencingpipeline (e.g., inferencing, object detection, feature detection,segmentation, image enhancement, calibration, etc.). In at least oneembodiment, an advanced processing and inferencing pipeline may bedefined based on selections of different containers that are desired orrequired for processing imaging data 1308, in addition to containersthat receive and configure imaging data for use by each container and/orfor use by facility 1302 after processing through a pipeline (e.g., toconvert outputs back to a usable data type). In at least one embodiment,a combination of containers within software 1318 (e.g., that make up apipeline) may be referred to as a virtual instrument (as described inmore detail herein), and a virtual instrument may leverage services 1320and hardware 1322 to execute some or all processing tasks ofapplications instantiated in containers.

In at least one embodiment, a data processing pipeline may receive inputdata (e.g., imaging data 1308) in a specific format in response to aninference request (e.g., a request from a user of deployment system1306). In at least one embodiment, input data may be representative ofone or more images, video, and/or other data representations generatedby one or more imaging devices. In at least one embodiment, data mayundergo pre-processing as part of data processing pipeline to preparedata for processing by one or more applications. In at least oneembodiment, post-processing may be performed on an output of one or moreinferencing tasks or other processing tasks of a pipeline to prepare anoutput data for a next application and/or to prepare output data fortransmission and/or use by a user (e.g., as a response to an inferencerequest). In at least one embodiment, inferencing tasks may be performedby one or more machine learning models, such as trained or deployedneural networks, which may include output models 1316 of training system1304.

In at least one embodiment, tasks of data processing pipeline may beencapsulated in a container(s) that each represents a discrete, fullyfunctional instantiation of an application and virtualized computingenvironment that is able to reference machine learning models. In atleast one embodiment, containers or applications may be published into aprivate (e.g., limited access) area of a container registry (describedin more detail herein), and trained or deployed models may be stored inmodel registry 1324 and associated with one or more applications. In atleast one embodiment, images of applications (e.g., container images)may be available in a container registry, and once selected by a userfrom a container registry for deployment in a pipeline, an image may beused to generate a container for an instantiation of an application foruse by a user's system.

In at least one embodiment, developers (e.g., software developers,clinicians, doctors, etc.) may develop, publish, and store applications(e.g., as containers) for performing image processing and/or inferencingon supplied data. In at least one embodiment, development, publishing,and/or storing may be performed using a software development kit (SDK)associated with a system (e.g., to ensure that an application and/orcontainer developed is compliant with or compatible with a system). Inat least one embodiment, an application that is developed may be testedlocally (e.g., at a first facility, on data from a first facility) withan SDK which may support at least some of services 1320 as a system(e.g., system 1400 of FIG. 14). In at least one embodiment, becauseDICOM objects may contain anywhere from one to hundreds of images orother data types, and due to a variation in data, a developer may beresponsible for managing (e.g., setting constructs for, buildingpre-processing into an application, etc.) extraction and preparation ofincoming data. In at least one embodiment, once validated by system 1400(e.g., for accuracy), an application may be available in a containerregistry for selection and/or implementation by a user to perform one ormore processing tasks with respect to data at a facility (e.g., a secondfacility) of a user.

In at least one embodiment, developers may then share applications orcontainers through a network for access and use by users of a system(e.g., system 1400 of FIG. 14). In at least one embodiment, completedand validated applications or containers may be stored in a containerregistry and associated machine learning models may be stored in modelregistry 1324. In at least one embodiment, a requesting entity—whoprovides an inference or image processing request—may browse a containerregistry and/or model registry 1324 for an application, container,dataset, machine learning model, etc., select a desired combination ofelements for inclusion in data processing pipeline, and submit animaging processing request. In at least one embodiment, a request mayinclude input data (and associated patient data, in some examples) thatis necessary to perform a request, and/or may include a selection ofapplication(s) and/or machine learning models to be executed inprocessing a request. In at least one embodiment, a request may then bepassed to one or more components of deployment system 1306 (e.g., acloud) to perform processing of data processing pipeline. In at leastone embodiment, processing by deployment system 1306 may includereferencing selected elements (e.g., applications, containers, models,etc.) from a container registry and/or model registry 1324. In at leastone embodiment, once results are generated by a pipeline, results may bereturned to a user for reference (e.g., for viewing in a viewingapplication suite executing on a local, on-premises workstation orterminal).

In at least one embodiment, to aid in processing or execution ofapplications or containers in pipelines, services 1320 may be leveraged.In at least one embodiment, services 1320 may include compute services,artificial intelligence (AI) services, visualization services, and/orother service types. In at least one embodiment, services 1320 mayprovide functionality that is common to one or more applications insoftware 1318, so functionality may be abstracted to a service that maybe called upon or leveraged by applications. In at least one embodiment,functionality provided by services 1320 may run dynamically and moreefficiently, while also scaling well by allowing applications to processdata in parallel (e.g., using a parallel computing platform 1430 (FIG.14)). In at least one embodiment, rather than each application thatshares a same functionality offered by a service 1320 being required tohave a respective instance of service 1320, service 1320 may be sharedbetween and among various applications. In at least one embodiment,services may include an inference server or engine that may be used forexecuting detection or segmentation tasks, as non-limiting examples. Inat least one embodiment, a model training service may be included thatmay provide machine learning model training and/or retrainingcapabilities. In at least one embodiment, a data augmentation servicemay further be included that may provide GPU accelerated data (e.g.,DICOM, RIS, CIS, REST compliant, RPC, raw, etc.) extraction, resizing,scaling, and/or other augmentation. In at least one embodiment, avisualization service may be used that may add image renderingeffects—such as ray-tracing, rasterization, denoising, sharpening,etc.—to add realism to two-dimensional (2D) and/or three-dimensional(3D) models. In at least one embodiment, virtual instrument services maybe included that provide for beam-forming, segmentation, inferencing,imaging, and/or support for other applications within pipelines ofvirtual instruments.

In at least one embodiment, where a service 1320 includes an AI service(e.g., an inference service), one or more machine learning models may beexecuted by calling upon (e.g., as an API call) an inference service(e.g., an inference server) to execute machine learning model(s), orprocessing thereof, as part of application execution. In at least oneembodiment, where another application includes one or more machinelearning models for segmentation tasks, an application may call upon aninference service to execute machine learning models for performing oneor more of processing operations associated with segmentation tasks. Inat least one embodiment, software 1318 implementing advanced processingand inferencing pipeline that includes segmentation application andanomaly detection application may be streamlined because eachapplication may call upon a same inference service to perform one ormore inferencing tasks.

In at least one embodiment, hardware 1322 may include GPUs, CPUs,graphics cards, an AI/deep learning system (e.g., an AI supercomputer,such as NVIDIA's DGX), a cloud platform, or a combination thereof. In atleast one embodiment, different types of hardware 1322 may be used toprovide efficient, purpose-built support for software 1318 and services1320 in deployment system 1306. In at least one embodiment, use of GPUprocessing may be implemented for processing locally (e.g., at facility1302), within an AI/deep learning system, in a cloud system, and/or inother processing components of deployment system 1306 to improveefficiency, accuracy, and efficacy of image processing and generation.In at least one embodiment, software 1318 and/or services 1320 may beoptimized for GPU processing with respect to deep learning, machinelearning, and/or high-performance computing, as non-limiting examples.In at least one embodiment, at least some of computing environment ofdeployment system 1306 and/or training system 1304 may be executed in adatacenter one or more supercomputers or high performance computingsystems, with GPU optimized software (e.g., hardware and softwarecombination of NVIDIA's DGX System). In at least one embodiment,hardware 1322 may include any number of GPUs that may be called upon toperform processing of data in parallel, as described herein. In at leastone embodiment, cloud platform may further include GPU processing forGPU-optimized execution of deep learning tasks, machine learning tasks,or other computing tasks. In at least one embodiment, cloud platform(e.g., NVIDIA's NGC) may be executed using an AI/deep learningsupercomputer(s) and/or GPU-optimized software (e.g., as provided onNVIDIA's DGX Systems) as a hardware abstraction and scaling platform. Inat least one embodiment, cloud platform may integrate an applicationcontainer clustering system or orchestration system (e.g., KUBERNETES)on multiple GPUs to enable seamless scaling and load balancing.

FIG. 14 is a system diagram for an example system 1400 for generatingand deploying an imaging deployment pipeline, in accordance with atleast one embodiment. In at least one embodiment, system 1400 may beused to implement process 1300 of FIG. 13 and/or other processesincluding advanced processing and inferencing pipelines. In at least oneembodiment, system 1400 may include training system 1304 and deploymentsystem 1306. In at least one embodiment, training system 1304 anddeployment system 1306 may be implemented using software 1318, services1320, and/or hardware 1322, as described herein.

In at least one embodiment, system 1400 (e.g., training system 1304and/or deployment system 1306) may implemented in a cloud computingenvironment (e.g., using cloud 1426). In at least one embodiment, system1400 may be implemented locally with respect to a healthcare servicesfacility, or as a combination of both cloud and local computingresources. In at least one embodiment, access to APIs in cloud 1426 maybe restricted to authorized users through enacted security measures orprotocols. In at least one embodiment, a security protocol may includeweb tokens that may be signed by an authentication (e.g., AuthN, AuthZ,Gluecon, etc.) service and may carry appropriate authorization. In atleast one embodiment, APIs of virtual instruments (described herein), orother instantiations of system 1400, may be restricted to a set ofpublic IPs that have been vetted or authorized for interaction.

In at least one embodiment, various components of system 1400 maycommunicate between and among one another using any of a variety ofdifferent network types, including but not limited to local areanetworks (LANs) and/or wide area networks (WANs) via wired and/orwireless communication protocols. In at least one embodiment,communication between facilities and components of system 1400 (e.g.,for transmitting inference requests, for receiving results of inferencerequests, etc.) may be communicated over data bus(ses), wireless dataprotocols (Wi-Fi), wired data protocols (e.g., Ethernet), etc.

In at least one embodiment, training system 1304 may execute trainingpipelines 1404, similar to those described herein with respect to FIG.13. In at least one embodiment, where one or more machine learningmodels are to be used in deployment pipelines 1410 by deployment system1306, training pipelines 1404 may be used to train or retrain one ormore (e.g. pre-trained) models, and/or implement one or more ofpre-trained models 1406 (e.g., without a need for retraining orupdating). In at least one embodiment, as a result of training pipelines1404, output model(s) 1316 may be generated. In at least one embodiment,training pipelines 1404 may include any number of processing steps, suchas but not limited to imaging data (or other input data) conversion oradaption In at least one embodiment, for different machine learningmodels used by deployment system 1306, different training pipelines 1404may be used. In at least one embodiment, training pipeline 1404 similarto a first example described with respect to FIG. 13 may be used for afirst machine learning model, training pipeline 1404 similar to a secondexample described with respect to FIG. 13 may be used for a secondmachine learning model, and training pipeline 1404 similar to a thirdexample described with respect to FIG. 13 may be used for a thirdmachine learning model. In at least one embodiment, any combination oftasks within training system 1304 may be used depending on what isrequired for each respective machine learning model. In at least oneembodiment, one or more of machine learning models may already betrained and ready for deployment so machine learning models may notundergo any processing by training system 1304, and may be implementedby deployment system 1306.

In at least one embodiment, output model(s) 1316 and/or pre-trainedmodel(s) 1406 may include any types of machine learning models dependingon implementation or embodiment. In at least one embodiment, and withoutlimitation, machine learning models used by system 1400 may includemachine learning model(s) using linear regression, logistic regression,decision trees, support vector machines (SVM), Naïve Bayes, k-nearestneighbor (Knn), K means clustering, random forest, dimensionalityreduction algorithms, gradient boosting algorithms, neural networks(e.g., auto-encoders, convolutional, recurrent, perceptrons, Long/ShortTerm Memory (LS™), Hopfield, Boltzmann, deep belief, deconvolutional,generative adversarial, liquid state machine, etc.), and/or other typesof machine learning models.

In at least one embodiment, training pipelines 1404 may includeAI-assisted annotation, as described in more detail herein with respectto at least FIG. 15B. In at least one embodiment, labeled data 1312(e.g., traditional annotation) may be generated by any number oftechniques. In at least one embodiment, labels or other annotations maybe generated within a drawing program (e.g., an annotation program), acomputer aided design (CAD) program, a labeling program, another type ofprogram suitable for generating annotations or labels for ground truth,and/or may be hand drawn, in some examples. In at least one embodiment,ground truth data may be synthetically produced (e.g., generated fromcomputer models or renderings), real produced (e.g., designed andproduced from real-world data), machine-automated (e.g., using featureanalysis and learning to extract features from data and then generatelabels), human annotated (e.g., labeler, or annotation expert, defineslocation of labels), and/or a combination thereof. In at least oneembodiment, for each instance of imaging data 1308 (or other data typeused by machine learning models), there may be corresponding groundtruth data generated by training system 1304. In at least oneembodiment, AI-assisted annotation may be performed as part ofdeployment pipelines 1410; either in addition to, or in lieu ofAI-assisted annotation included in training pipelines 1404. In at leastone embodiment, system 1400 may include a multi-layer platform that mayinclude a software layer (e.g., software 1318) of diagnosticapplications (or other application types) that may perform one or moremedical imaging and diagnostic functions. In at least one embodiment,system 1400 may be communicatively coupled to (e.g., via encryptedlinks) PACS server networks of one or more facilities. In at least oneembodiment, system 1400 may be configured to access and referenced datafrom PACS servers to perform operations, such as training machinelearning models, deploying machine learning models, image processing,inferencing, and/or other operations.

In at least one embodiment, a software layer may be implemented as asecure, encrypted, and/or authenticated API through which applicationsor containers may be invoked (e.g., called) from an externalenvironment(s) (e.g., facility 1302). In at least one embodiment,applications may then call or execute one or more services 1320 forperforming compute, AI, or visualization tasks associated withrespective applications, and software 1318 and/or services 1320 mayleverage hardware 1322 to perform processing tasks in an effective andefficient manner.

In at least one embodiment, deployment system 1306 may executedeployment pipelines 1410. In at least one embodiment, deploymentpipelines 1410 may include any number of applications that may besequentially, non-sequentially, or otherwise applied to imaging data(and/or other data types) generated by imaging devices, sequencingdevices, genomics devices, etc.—including AI-assisted annotation, asdescribed above. In at least one embodiment, as described herein, adeployment pipeline 1410 for an individual device may be referred to asa virtual instrument for a device (e.g., a virtual ultrasoundinstrument, a virtual CT scan instrument, a virtual sequencinginstrument, etc.). In at least one embodiment, for a single device,there may be more than one deployment pipeline 1410 depending oninformation desired from data generated by a device. In at least oneembodiment, where detections of anomalies are desired from an Millmachine, there may be a first deployment pipeline 1410, and where imageenhancement is desired from output of an Mill machine, there may be asecond deployment pipeline 1410.

In at least one embodiment, an image generation application may includea processing task that includes use of a machine learning model. In atleast one embodiment, a user may desire to use their own machinelearning model, or to select a machine learning model from modelregistry 1324. In at least one embodiment, a user may implement theirown machine learning model or select a machine learning model forinclusion in an application for performing a processing task. In atleast one embodiment, applications may be selectable and customizable,and by defining constructs of applications, deployment andimplementation of applications for a particular user are presented as amore seamless user experience. In at least one embodiment, by leveragingother features of system 1400—such as services 1320 and hardware1322—deployment pipelines 1410 may be even more user friendly, providefor easier integration, and produce more accurate, efficient, and timelyresults.

In at least one embodiment, deployment system 1306 may include a userinterface 1414 (e.g., a graphical user interface, a web interface, etc.)that may be used to select applications for inclusion in deploymentpipeline(s) 1410, arrange applications, modify or change applications orparameters or constructs thereof, use and interact with deploymentpipeline(s) 1410 during set-up and/or deployment, and/or to otherwiseinteract with deployment system 1306. In at least one embodiment,although not illustrated with respect to training system 1304, userinterface 1414 (or a different user interface) may be used for selectingmodels for use in deployment system 1306, for selecting models fortraining, or retraining, in training system 1304, and/or for otherwiseinteracting with training system 1304.

In at least one embodiment, pipeline manager 1412 may be used, inaddition to an application orchestration system 1428, to manageinteraction between applications or containers of deployment pipeline(s)1410 and services 1320 and/or hardware 1322. In at least one embodiment,pipeline manager 1412 may be configured to facilitate interactions fromapplication to application, from application to service 1320, and/orfrom application or service to hardware 1322. In at least oneembodiment, although illustrated as included in software 1318, this isnot intended to be limiting, and in some examples (e.g., as illustratedin FIG. 12 cc) pipeline manager 1412 may be included in services 1320.In at least one embodiment, application orchestration system 1428 (e.g.,Kubernetes, DOCKER, etc.) may include a container orchestration systemthat may group applications into containers as logical units forcoordination, management, scaling, and deployment. In at least oneembodiment, by associating applications from deployment pipeline(s) 1410(e.g., a reconstruction application, a segmentation application, etc.)with individual containers, each application may execute in aself-contained environment (e.g., at a kernel level) to increase speedand efficiency.

In at least one embodiment, each application and/or container (or imagethereof) may be individually developed, modified, and deployed (e.g., afirst user or developer may develop, modify, and deploy a firstapplication and a second user or developer may develop, modify, anddeploy a second application separate from a first user or developer),which may allow for focus on, and attention to, a task of a singleapplication and/or container(s) without being hindered by tasks ofanother application(s) or container(s). In at least one embodiment,communication, and cooperation between different containers orapplications may be aided by pipeline manager 1412 and applicationorchestration system 1428. In at least one embodiment, so long as anexpected input and/or output of each container or application is knownby a system (e.g., based on constructs of applications or containers),application orchestration system 1428 and/or pipeline manager 1412 mayfacilitate communication among and between, and sharing of resourcesamong and between, each of applications or containers. In at least oneembodiment, because one or more of applications or containers indeployment pipeline(s) 1410 may share same services and resources,application orchestration system 1428 may orchestrate, load balance, anddetermine sharing of services or resources between and among variousapplications or containers. In at least one embodiment, a scheduler maybe used to track resource requirements of applications or containers,current usage or planned usage of these resources, and resourceavailability. In at least one embodiment, a scheduler may thus allocateresources to different applications and distribute resources between andamong applications in view of requirements and availability of a system.In some examples, a scheduler (and/or other component of applicationorchestration system 1428) may determine resource availability anddistribution based on constraints imposed on a system (e.g., userconstraints), such as quality of service (QoS), urgency of need for dataoutputs (e.g., to determine whether to execute real-time processing ordelayed processing), etc.

In at least one embodiment, services 1320 leveraged by and shared byapplications or containers in deployment system 1306 may include computeservices 1416, AI services 1418, visualization services 1420, and/orother service types. In at least one embodiment, applications may call(e.g., execute) one or more of services 1320 to perform processingoperations for an application. In at least one embodiment, computeservices 1416 may be leveraged by applications to performsuper-computing or other high-performance computing (HPC) tasks. In atleast one embodiment, compute service(s) 1416 may be leveraged toperform parallel processing (e.g., using a parallel computing platform1430) for processing data through one or more of applications and/or oneor more tasks of a single application, substantially simultaneously. Inat least one embodiment, parallel computing platform 1430 (e.g.,NVIDIA's CUDA) may enable general purpose computing on GPUs (GPGPU)(e.g., GPUs 1422). In at least one embodiment, a software layer ofparallel computing platform 1430 may provide access to virtualinstruction sets and parallel computational elements of GPUs, forexecution of compute kernels. In at least one embodiment, parallelcomputing platform 1430 may include memory and, in some embodiments, amemory may be shared between and among multiple containers, and/orbetween and among different processing tasks within a single container.In at least one embodiment, inter-process communication (IPC) calls maybe generated for multiple containers and/or for multiple processeswithin a container to use same data from a shared segment of memory ofparallel computing platform 1430 (e.g., where multiple different stagesof an application or multiple applications are processing sameinformation). In at least one embodiment, rather than making a copy ofdata and moving data to different locations in memory (e.g., aread/write operation), same data in same location of a memory may beused for any number of processing tasks (e.g., at a same time, atdifferent times, etc.). In at least one embodiment, as data is used togenerate new data as a result of processing, this information of a newlocation of data may be stored and shared between various applications.In at least one embodiment, location of data and a location of updatedor modified data may be part of a definition of how a payload isunderstood within containers.

In at least one embodiment, AI services 1418 may be leveraged to performinferencing services for executing machine learning model(s) associatedwith applications (e.g., tasked with performing one or more processingtasks of an application). In at least one embodiment, AI services 1418may leverage AI system 1424 to execute machine learning model(s) (e.g.,neural networks, such as CNNs) for segmentation, reconstruction, objectdetection, feature detection, classification, and/or other inferencingtasks. In at least one embodiment, applications of deploymentpipeline(s) 1410 may use one or more of output models 1316 from trainingsystem 1304 and/or other models of applications to perform inference onimaging data. In at least one embodiment, two or more examples ofinferencing using application orchestration system 1428 (e.g., ascheduler) may be available. In at least one embodiment, a firstcategory may include a high priority/low latency path that may achievehigher service level agreements, such as for performing inference onurgent requests during an emergency, or for a radiologist duringdiagnosis. In at least one embodiment, a second category may include astandard priority path that may be used for requests that may benon-urgent or where analysis may be performed at a later time. In atleast one embodiment, application orchestration system 1428 maydistribute resources (e.g., services 1320 and/or hardware 1322) based onpriority paths for different inferencing tasks of AI services 1418.

In at least one embodiment, shared storage may be mounted to AI services1418 within system 1400. In at least one embodiment, shared storage mayoperate as a cache (or other storage device type) and may be used toprocess inference requests from applications. In at least oneembodiment, when an inference request is submitted, a request may bereceived by a set of API instances of deployment system 1306, and one ormore instances may be selected (e.g., for best fit, for load balancing,etc.) to process a request. In at least one embodiment, to process arequest, a request may be entered into a database, a machine learningmodel may be located from model registry 1324 if not already in a cache,a validation step may ensure appropriate machine learning model isloaded into a cache (e.g., shared storage), and/or a copy of a model maybe saved to a cache. In at least one embodiment, a scheduler (e.g., ofpipeline manager 1412) may be used to launch an application that isreferenced in a request if an application is not already running or ifthere are not enough instances of an application. In at least oneembodiment, if an inference server is not already launched to execute amodel, an inference server may be launched. Any number of inferenceservers may be launched per model. In at least one embodiment, in a pullmodel, in which inference servers are clustered, models may be cachedwhenever load balancing is advantageous. In at least one embodiment,inference servers may be statically loaded in corresponding, distributedservers.

In at least one embodiment, inferencing may be performed using aninference server that runs in a container. In at least one embodiment,an instance of an inference server may be associated with a model (andoptionally a plurality of versions of a model). In at least oneembodiment, if an instance of an inference server does not exist when arequest to perform inference on a model is received, a new instance maybe loaded. In at least one embodiment, when starting an inferenceserver, a model may be passed to an inference server such that a samecontainer may be used to serve different models so long as inferenceserver is running as a different instance.

In at least one embodiment, during application execution, an inferencerequest for a given application may be received, and a container (e.g.,hosting an instance of an inference server) may be loaded (if notalready), and a start procedure may be called. In at least oneembodiment, pre-processing logic in a container may load, decode, and/orperform any additional pre-processing on incoming data (e.g., using aCPU(s) and/or GPU(s)). In at least one embodiment, once data is preparedfor inference, a container may perform inference as necessary on data.In at least one embodiment, this may include a single inference call onone image (e.g., a hand X-ray), or may require inference on hundreds ofimages (e.g., a chest CT). In at least one embodiment, an applicationmay summarize results before completing, which may include, withoutlimitation, a single confidence score, pixel level-segmentation,voxel-level segmentation, generating a visualization, or generating textto summarize findings. In at least one embodiment, different models orapplications may be assigned different priorities. For example, somemodels may have a real-time (TAT<1 min) priority while others may havelower priority (e.g., TAT<10 min). In at least one embodiment, modelexecution times may be measured from requesting institution or entityand may include partner network traversal time, as well as execution onan inference service.

In at least one embodiment, transfer of requests between services 1320and inference applications may be hidden behind a software developmentkit (SDK), and robust transport may be provide through a queue. In atleast one embodiment, a request will be placed in a queue via an API foran individual application/tenant ID combination and an SDK will pull arequest from a queue and give a request to an application. In at leastone embodiment, a name of a queue may be provided in an environment fromwhere an SDK will pick it up. In at least one embodiment, asynchronouscommunication through a queue may be useful as it may allow any instanceof an application to pick up work as it becomes available. Results maybe transferred back through a queue, to ensure no data is lost. In atleast one embodiment, queues may also provide an ability to segmentwork, as highest priority work may go to a queue with most instances ofan application connected to it, while lowest priority work may go to aqueue with a single instance connected to it that processes tasks in anorder received. In at least one embodiment, an application may run on aGPU-accelerated instance generated in cloud 1426, and an inferenceservice may perform inferencing on a GPU.

In at least one embodiment, visualization services 1420 may be leveragedto generate visualizations for viewing outputs of applications and/ordeployment pipeline(s) 1410. In at least one embodiment, GPUs 1422 maybe leveraged by visualization services 1420 to generate visualizations.In at least one embodiment, rendering effects, such as ray-tracing, maybe implemented by visualization services 1420 to generate higher qualityvisualizations. In at least one embodiment, visualizations may include,without limitation, 2D image renderings, 3D volume renderings, 3D volumereconstruction, 2D tomographic slices, virtual reality displays,augmented reality displays, etc. In at least one embodiment, virtualizedenvironments may be used to generate a virtual interactive display orenvironment (e.g., a virtual environment) for interaction by users of asystem (e.g., doctors, nurses, radiologists, etc.). In at least oneembodiment, visualization services 1420 may include an internalvisualizer, cinematics, and/or other rendering or image processingcapabilities or functionality (e.g., ray tracing, rasterization,internal optics, etc.).

In at least one embodiment, hardware 1322 may include GPUs 1422, AIsystem 1424, cloud 1426, and/or any other hardware used for executingtraining system 1304 and/or deployment system 1306. In at least oneembodiment, GPUs 1422 (e.g., NVIDIA's TESLA and/or QUADRO GPUs) mayinclude any number of GPUs that may be used for executing processingtasks of compute services 1416, AI services 1418, visualization services1420, other services, and/or any of features or functionality ofsoftware 1318. For example, with respect to AI services 1418, GPUs 1422may be used to perform pre-processing on imaging data (or other datatypes used by machine learning models), post-processing on outputs ofmachine learning models, and/or to perform inferencing (e.g., to executemachine learning models). In at least one embodiment, cloud 1426, AIsystem 1424, and/or other components of system 1400 may use GPUs 1422.In at least one embodiment, cloud 1426 may include a GPU-optimizedplatform for deep learning tasks. In at least one embodiment, AI system1424 may use GPUs, and cloud 1426—or at least a portion tasked with deeplearning or inferencing—may be executed using one or more AI systems1424. As such, although hardware 1322 is illustrated as discretecomponents, this is not intended to be limiting, and any components ofhardware 1322 may be combined with, or leveraged by, any othercomponents of hardware 1322.

In at least one embodiment, AI system 1424 may include a purpose-builtcomputing system (e.g., a super-computer or an HPC) configured forinferencing, deep learning, machine learning, and/or other artificialintelligence tasks. In at least one embodiment, AI system 1424 (e.g.,NVIDIA's DGX) may include GPU-optimized software (e.g., a softwarestack) that may be executed using a plurality of GPUs 1422, in additionto CPUs, RAM, storage, and/or other components, features, orfunctionality. In at least one embodiment, one or more AI systems 1424may be implemented in cloud 1426 (e.g., in a data center) for performingsome or all of AI-based processing tasks of system 1400.

In at least one embodiment, cloud 1426 may include a GPU-acceleratedinfrastructure (e.g., NVIDIA's NGC) that may provide a GPU-optimizedplatform for executing processing tasks of system 1400. In at least oneembodiment, cloud 1426 may include an AI system(s) 1424 for performingone or more of AI-based tasks of system 1400 (e.g., as a hardwareabstraction and scaling platform). In at least one embodiment, cloud1426 may integrate with application orchestration system 1428 leveragingmultiple GPUs to enable seamless scaling and load balancing between andamong applications and services 1320. In at least one embodiment, cloud1426 may tasked with executing at least some of services 1320 of system1400, including compute services 1416, AI services 1418, and/orvisualization services 1420, as described herein. In at least oneembodiment, cloud 1426 may perform small and large batch inference(e.g., executing NVIDIA's TENSOR RT), provide an accelerated parallelcomputing API and platform 1430 (e.g., NVIDIA's CUDA), executeapplication orchestration system 1428 (e.g., KUBERNETES), provide agraphics rendering API and platform (e.g., for ray-tracing, 2D graphics,3D graphics, and/or other rendering techniques to produce higher qualitycinematics), and/or may provide other functionality for system 1400.

FIG. 15A illustrates a data flow diagram for a process 1500 to train,retrain, or update a machine learning model, in accordance with at leastone embodiment. In at least one embodiment, process 1500 may be executedusing, as a non-limiting example, system 1400 of FIG. 14. In at leastone embodiment, process 1500 may leverage services 1320 and/or hardware1322 of system 1400, as described herein. In at least one embodiment,refined models 1512 generated by process 1500 may be executed bydeployment system 1306 for one or more containerized applications indeployment pipelines 1410.

In at least one embodiment, model training 1314 may include retrainingor updating an initial model 1504 (e.g., a pre-trained model) using newtraining data (e.g., new input data, such as customer dataset 1506,and/or new ground truth data associated with input data). In at leastone embodiment, to retrain, or update, initial model 1504, output orloss layer(s) of initial model 1504 may be reset, or deleted, and/orreplaced with an updated or new output or loss layer(s). In at least oneembodiment, initial model 1504 may have previously fine-tuned parameters(e.g., weights and/or biases) that remain from prior training, sotraining or retraining 1314 may not take as long or require as muchprocessing as training a model from scratch. In at least one embodiment,during model training 1314, by having reset or replaced output or losslayer(s) of initial model 1504, parameters may be updated and re-tunedfor a new data set based on loss calculations associated with accuracyof output or loss layer(s) at generating predictions on new, customerdataset 1506 (e.g., image data 1308 of FIG. 13).

In at least one embodiment, pre-trained models 1406 may be stored in adata store, or registry (e.g., model registry 1324 of FIG. 13). In atleast one embodiment, pre-trained models 1406 may have been trained, atleast in part, at one or more facilities other than a facility executingprocess 1500. In at least one embodiment, to protect privacy and rightsof patients, subjects, or clients of different facilities, pre-trainedmodels 1406 may have been trained, on-premise, using customer or patientdata generated on-premise. In at least one embodiment, pre-trainedmodels 1406 may be trained using cloud 1426 and/or other hardware 1322,but confidential, privacy protected patient data may not be transferredto, used by, or accessible to any components of cloud 1426 (or other offpremise hardware). In at least one embodiment, where a pre-trained model1406 is trained at using patient data from more than one facility,pre-trained model 1406 may have been individually trained for eachfacility prior to being trained on patient or customer data from anotherfacility. In at least one embodiment, such as where a customer orpatient data has been released of privacy concerns (e.g., by waiver, forexperimental use, etc.), or where a customer or patient data is includedin a public data set, a customer or patient data from any number offacilities may be used to train pre-trained model 1406 on-premise and/oroff premise, such as in a datacenter or other cloud computinginfrastructure.

In at least one embodiment, when selecting applications for use indeployment pipelines 1410, a user may also select machine learningmodels to be used for specific applications. In at least one embodiment,a user may not have a model for use, so a user may select a pre-trainedmodel 1406 to use with an application. In at least one embodiment,pre-trained model 1406 may not be optimized for generating accurateresults on customer dataset 1506 of a facility of a user (e.g., based onpatient diversity, demographics, types of medical imaging devices used,etc.). In at least one embodiment, prior to deploying pre-trained model1406 into deployment pipeline 1410 for use with an application(s),pre-trained model 1406 may be updated, retrained, and/or fine-tuned foruse at a respective facility.

In at least one embodiment, a user may select pre-trained model 1406that is to be updated, retrained, and/or fine-tuned, and pre-trainedmodel 1406 may be referred to as initial model 1504 for training system1304 within process 1500. In at least one embodiment, customer dataset1506 (e.g., imaging data, genomics data, sequencing data, or other datatypes generated by devices at a facility) may be used to perform modeltraining 1314 (which may include, without limitation, transfer learning)on initial model 1504 to generate refined model 1512. In at least oneembodiment, ground truth data corresponding to customer dataset 1506 maybe generated by training system 1304. In at least one embodiment, groundtruth data may be generated, at least in part, by clinicians,scientists, doctors, practitioners, at a facility (e.g., as labeledclinic data 1312 of FIG. 13).

In at least one embodiment, AI-assisted annotation 1310 may be used insome examples to generate ground truth data. In at least one embodiment,AI-assisted annotation 1310 (e.g., implemented using an AI-assistedannotation SDK) may leverage machine learning models (e.g., neuralnetworks) to generate suggested or predicted ground truth data for acustomer dataset. In at least one embodiment, user 1510 may useannotation tools within a user interface (a graphical user interface(GUI)) on computing device 1508.

In at least one embodiment, user 1510 may interact with a GUI viacomputing device 1508 to edit or fine-tune (auto)annotations. In atleast one embodiment, a polygon editing feature may be used to movevertices of a polygon to more accurate or fine-tuned locations.

In at least one embodiment, once customer dataset 1506 has associatedground truth data, ground truth data (e.g., from AI-assisted annotation,manual labeling, etc.) may be used by during model training 1314 togenerate refined model 1512. In at least one embodiment, customerdataset 1506 may be applied to initial model 1504 any number of times,and ground truth data may be used to update parameters of initial model1504 until an acceptable level of accuracy is attained for refined model1512. In at least one embodiment, once refined model 1512 is generated,refined model 1512 may be deployed within one or more deploymentpipelines 1410 at a facility for performing one or more processing taskswith respect to medical imaging data.

In at least one embodiment, refined model 1512 may be uploaded topre-trained models 1406 in model registry 1324 to be selected by anotherfacility. In at least one embodiment, his process may be completed atany number of facilities such that refined model 1512 may be furtherrefined on new datasets any number of times to generate a more universalmodel.

FIG. 15B is an example illustration of a client-server architecture 1532to enhance annotation tools with pre-trained annotation models, inaccordance with at least one embodiment. In at least one embodiment,AI-assisted annotation tools 1536 may be instantiated based on aclient-server architecture 1532. In at least one embodiment, annotationtools 1536 in imaging applications may aid radiologists, for example,identify organs and abnormalities. In at least one embodiment, imagingapplications may include software tools that help user 1510 to identify,as a non-limiting example, a few extreme points on a particular organ ofinterest in raw images 1534 (e.g., in a 3D MRI or CT scan) and receiveauto-annotated results for all 2D slices of a particular organ. In atleast one embodiment, results may be stored in a data store as trainingdata 1538 and used as (for example and without limitation) ground truthdata for training. In at least one embodiment, when computing device1508 sends extreme points for AI-assisted annotation 1310, a deeplearning model, for example, may receive this data as input and returninference results of a segmented organ or abnormality. In at least oneembodiment, pre-instantiated annotation tools, such as AI-AssistedAnnotation Tool 1536B in FIG. 15B, may be enhanced by making API calls(e.g., API Call 1544) to a server, such as an Annotation AssistantServer 1540 that may include a set of pre-trained models 1542 stored inan annotation model registry, for example. In at least one embodiment,an annotation model registry may store pre-trained models 1542 (e.g.,machine learning models, such as deep learning models) that arepre-trained to perform AI-assisted annotation on a particular organ orabnormality. These models may be further updated by using trainingpipelines 1404. In at least one embodiment, pre-installed annotationtools may be improved over time as new labeled clinic data 1312 isadded.

Such components can be used to train an inverse graphics network using aset of images generated by a generator network, where aspects of objectsare kept fixed while pose or view information is varied between imagesof the set.

Other variations are within spirit of present disclosure. Thus, whiledisclosed techniques are susceptible to various modifications andalternative constructions, certain illustrated embodiments thereof areshown in drawings and have been described above in detail. It should beunderstood, however, that there is no intention to limit disclosure tospecific form or forms disclosed, but on contrary, intention is to coverall modifications, alternative constructions, and equivalents fallingwithin spirit and scope of disclosure, as defined in appended claims.

Use of terms “a” and “an” and “the” and similar referents in context ofdescribing disclosed embodiments (especially in context of followingclaims) are to be construed to cover both singular and plural, unlessotherwise indicated herein or clearly contradicted by context, and notas a definition of a term. Terms “comprising,” “having,” “including,”and “containing” are to be construed as open-ended terms (meaning“including, but not limited to,”) unless otherwise noted. Term“connected,” when unmodified and referring to physical connections, isto be construed as partly or wholly contained within, attached to, orjoined together, even if there is something intervening. Recitation ofranges of values herein are merely intended to serve as a shorthandmethod of referring individually to each separate value falling withinrange, unless otherwise indicated herein and each separate value isincorporated into specification as if it were individually recitedherein. Use of term “set” (e.g., “a set of items”) or “subset,” unlessotherwise noted or contradicted by context, is to be construed as anonempty collection comprising one or more members. Further, unlessotherwise noted or contradicted by context, term “subset” of acorresponding set does not necessarily denote a proper subset ofcorresponding set, but subset and corresponding set may be equal.

Conjunctive language, such as phrases of form “at least one of A, B, andC,” or “at least one of A, B and C,” unless specifically statedotherwise or otherwise clearly contradicted by context, is otherwiseunderstood with context as used in general to present that an item,term, etc., may be either A or B or C, or any nonempty subset of set ofA and B and C. For instance, in illustrative example of a set havingthree members, conjunctive phrases “at least one of A, B, and C” and “atleast one of A, B and C” refer to any of following sets: {A}, {B}, {C},{A, B}, {A, C}, {B, C}, {A, B, C}. Thus, such conjunctive language isnot generally intended to imply that certain embodiments require atleast one of A, at least one of B, and at least one of C each to bepresent. In addition, unless otherwise noted or contradicted by context,term “plurality” indicates a state of being plural (e.g., “a pluralityof items” indicates multiple items). A plurality is at least two items,but can be more when so indicated either explicitly or by context.Further, unless stated otherwise or otherwise clear from context, phrase“based on” means “based at least in part on” and not “based solely on.”

Operations of processes described herein can be performed in anysuitable order unless otherwise indicated herein or otherwise clearlycontradicted by context. In at least one embodiment, a process such asthose processes described herein (or variations and/or combinationsthereof) is performed under control of one or more computer systemsconfigured with executable instructions and is implemented as code(e.g., executable instructions, one or more computer programs or one ormore applications) executing collectively on one or more processors, byhardware or combinations thereof. In at least one embodiment, code isstored on a computer-readable storage medium, for example, in form of acomputer program comprising a plurality of instructions executable byone or more processors. In at least one embodiment, a computer-readablestorage medium is a non-transitory computer-readable storage medium thatexcludes transitory signals (e.g., a propagating transient electric orelectromagnetic transmission) but includes non-transitory data storagecircuitry (e.g., buffers, cache, and queues) within transceivers oftransitory signals. In at least one embodiment, code (e.g., executablecode or source code) is stored on a set of one or more non-transitorycomputer-readable storage media having stored thereon executableinstructions (or other memory to store executable instructions) that,when executed (i.e., as a result of being executed) by one or moreprocessors of a computer system, cause computer system to performoperations described herein. A set of non-transitory computer-readablestorage media, in at least one embodiment, comprises multiplenon-transitory computer-readable storage media and one or more ofindividual non-transitory storage media of multiple non-transitorycomputer-readable storage media lack all of code while multiplenon-transitory computer-readable storage media collectively store all ofcode. In at least one embodiment, executable instructions are executedsuch that different instructions are executed by differentprocessors—for example, a non-transitory computer-readable storagemedium store instructions and a main central processing unit (“CPU”)executes some of instructions while a graphics processing unit (“GPU”)executes other instructions. In at least one embodiment, differentcomponents of a computer system have separate processors and differentprocessors execute different subsets of instructions.

Accordingly, in at least one embodiment, computer systems are configuredto implement one or more services that singly or collectively performoperations of processes described herein and such computer systems areconfigured with applicable hardware and/or software that enableperformance of operations. Further, a computer system that implements atleast one embodiment of present disclosure is a single device and, inanother embodiment, is a distributed computer system comprising multipledevices that operate differently such that distributed computer systemperforms operations described herein and such that a single device doesnot perform all operations.

Use of any and all examples, or exemplary language (e.g., “such as”)provided herein, is intended merely to better illuminate embodiments ofdisclosure and does not pose a limitation on scope of disclosure unlessotherwise claimed. No language in specification should be construed asindicating any non-claimed element as essential to practice ofdisclosure.

All references, including publications, patent applications, andpatents, cited herein are hereby incorporated by reference to sameextent as if each reference were individually and specifically indicatedto be incorporated by reference and were set forth in its entiretyherein.

In description and claims, terms “coupled” and “connected,” along withtheir derivatives, may be used. It should be understood that these termsmay be not intended as synonyms for each other. Rather, in particularexamples, “connected” or “coupled” may be used to indicate that two ormore elements are in direct or indirect physical or electrical contactwith each other. “Coupled” may also mean that two or more elements arenot in direct contact with each other, but yet still co-operate orinteract with each other.

Unless specifically stated otherwise, it may be appreciated thatthroughout specification terms such as “processing,” “computing,”“calculating,” “determining,” or like, refer to action and/or processesof a computer or computing system, or similar electronic computingdevice, that manipulate and/or transform data represented as physical,such as electronic, quantities within computing system's registersand/or memories into other data similarly represented as physicalquantities within computing system's memories, registers or other suchinformation storage, transmission or display devices.

In a similar manner, term “processor” may refer to any device or portionof a device that processes electronic data from registers and/or memoryand transform that electronic data into other electronic data that maybe stored in registers and/or memory. As non-limiting examples,“processor” may be a CPU or a GPU. A “computing platform” may compriseone or more processors. As used herein, “software” processes mayinclude, for example, software and/or hardware entities that performwork over time, such as tasks, threads, and intelligent agents. Also,each process may refer to multiple processes, for carrying outinstructions in sequence or in parallel, continuously or intermittently.Terms “system” and “method” are used herein interchangeably insofar assystem may embody one or more methods and methods may be considered asystem.

In present document, references may be made to obtaining, acquiring,receiving, or inputting analog or digital data into a subsystem,computer system, or computer-implemented machine. Obtaining, acquiring,receiving, or inputting analog and digital data can be accomplished in avariety of ways such as by receiving data as a parameter of a functioncall or a call to an application programming interface. In someimplementations, process of obtaining, acquiring, receiving, orinputting analog or digital data can be accomplished by transferringdata via a serial or parallel interface. In another implementation,process of obtaining, acquiring, receiving, or inputting analog ordigital data can be accomplished by transferring data via a computernetwork from providing entity to acquiring entity. References may alsobe made to providing, outputting, transmitting, sending, or presentinganalog or digital data. In various examples, process of providing,outputting, transmitting, sending, or presenting analog or digital datacan be accomplished by transferring data as an input or output parameterof a function call, a parameter of an application programming interfaceor interprocess communication mechanism.

Although discussion above sets forth example implementations ofdescribed techniques, other architectures may be used to implementdescribed functionality, and are intended to be within scope of thisdisclosure. Furthermore, although specific distributions ofresponsibilities are defined above for purposes of discussion, variousfunctions and responsibilities might be distributed and divided indifferent ways, depending on circumstances.

Furthermore, although subject matter has been described in languagespecific to structural features and/or methodological acts, it is to beunderstood that subject matter claimed in appended claims is notnecessarily limited to specific features or acts described. Rather,specific features and acts are disclosed as exemplary forms ofimplementing the claims.

What is claimed is:
 1. A computer-implemented method, comprising:providing a two-dimensional image of an object as input to a generativenetwork; generating, using the generative network, a set of view imagesof the object represented from different views; providing, as input toan inverse graphics network, the set of view images and information forthe different views; determining, for individual view images of the setusing the inverse graphics network, a set of three-dimensionalinformation; rendering, for the individual view images of the set, arepresentation of the object using the set of three-dimensionalinformation and the respective view information; comparing the renderedrepresentations against corresponding ground truth training data todetermine at least one loss value, the ground truth training data basedat least in part on annotation data; and adjusting one or more networkparameters for the inverse graphics network based at least in part uponthe at least one loss value.
 2. The computer-implemented method of claim1, further comprising: providing at least a subset of therepresentations of the object, rendered by the inverse graphics network,as training data to further train the generative network.
 3. Thecomputer-implemented method of claim 2, further comprising: training theinverse graphics network and the generative network together using acommon loss function.
 4. The computer-implemented method of claim 1,wherein the generative network is a style generative adversarial networkenabling only camera view-related features to be adjusted for generatingthe set of view images.
 5. The computer-implemented method of claim 1,further comprising: using a selection matrix to reduce a dimensionalityof image features to be included in a latent code to be used to renderthe representation of the object.
 6. The computer-implemented method ofclaim 5, further comprising: rendering the representation of the objectbased, at least in part, upon the latent code and using a differentiablerenderer.
 7. The computer-implemented method of claim 5, wherein thelatent code includes camera features for the corresponding view.
 8. Thecomputer-implemented method of claim 1, wherein the three-dimensionalinformation for the object includes at least one of a shape, texture,lighting, or background for the object.
 9. The computer-implementedmethod of claim 1, wherein the two-dimensional image input to thegenerative network is annotated with weakly accurate camera informationcorresponding to a subset of object features.
 10. A system, comprising:at least one processor; and memory including instructions that, whenexecuted by the at least one processor, cause the system to: provide atwo-dimensional image of an object as input to a generative network;generate, using the generative network, a set of view images of theobject represented from different views; provide, as input to an inversegraphics network, the set of view images and information for thedifferent views; determine, for individual view images of the set usingthe inverse graphics network, a set of three-dimensional information;render, for the individual view images of the set, a representation ofthe object using the set of three-dimensional information and therespective view information; compare the rendered representationsagainst corresponding ground truth training data to determine at leastone loss value, the ground truth training data based at least in part onannotation data; and adjust one or more network parameters for theinverse graphics network based at least in part upon the at least oneloss value.
 11. The system of claim 10, wherein the instructions whenexecuted further cause the system to: provide at least a subset of therepresentations of the object, rendered by the inverse graphics network,as training data to further train the generative network.
 12. The systemof claim 11, wherein the instructions when executed further cause thesystem to: train the inverse graphics network and the generative networktogether using a common loss function.
 13. The system of claim 10,wherein the generative network is a style generative adversarial networkenabling only camera view-related features to be adjusted for generatingthe set of view images.
 14. The system of claim 10, wherein theinstructions when executed further cause the system to: use a selectionmatrix to reduce a dimensionality of image features to be included in alatent code to be used to render the representation of the object; andrender the representation of the object based, at least in part, uponthe latent code using a differentiable renderer.
 15. The system of claim10, wherein the system comprises at least one of: a system forperforming graphical rendering operations; a system for performingsimulation operations; a system for performing simulation operations totest or validate autonomous machine applications; a system forperforming deep learning operations; a system implemented using an edgedevice; a system incorporating one or more Virtual Machines (VMs); asystem implemented at least partially in a data center; or a systemimplemented at least partially using cloud computing resources.
 16. Acomputer-implemented method, comprising: receiving a two-dimensionalimage; and generating a three-dimensional representation of thetwo-dimensional image using an inverse graphics network, the inversegraphics network trained at least in part by: generating, using agenerative network and a two-dimensional input image of an object, a setof view images of the object represented from different views;determining, for individual view images of the set using the inversegraphics network, a set of three-dimensional information; rendering, forthe individual view images of the set, a representation of the objectusing the set of three-dimensional information and information for therespective view; comparing the rendered representations againstcorresponding ground truth training representations to determine atleast one loss value, the ground truth training representations based atleast in part on annotation data; and adjusting one or more networkparameters for the inverse graphics network based at least in part uponthe at least one loss value.
 17. The computer-implemented method ofclaim 16, further comprising: providing at least a subset of therepresentations of the object, rendered by the inverse graphics network,as training data to further train the generative network.
 18. Thecomputer-implemented method of claim 17, further comprising: trainingthe inverse graphics network and the generative network together using acommon loss function.
 19. The computer-implemented method of claim 16,wherein the generative network is a style generative adversarial networkenabling only camera view-related features to be adjusted for generatingthe set of view images.
 20. The computer-implemented method of claim 16,wherein the two-dimensional input image is annotated with weaklyaccurate camera information corresponding to a subset of objectfeatures.